Using Excel to Detect Fraud

Using Excel to Detect Fraud
ASA Research
Using Excel
to Detect Fraud
J. Carlton Collins, CPA
ASA Research - Atlanta, Georgia
770.842.5902
Carlton@ASAResearch.com
Using Excel to Detect Fraud
Table of Contents
1. Random Numbers – Attendees will learn how to generate random numbers to be used for
statistical sampling purposes. ................................................................................................ 5
2. Consolidating Data – Attendees will learn how to consolidate data (such as budgets, financial
reports, departmental reports, inventory lists, salesperson reports, location reports, etc.)
using four different techniques as follows: Formulas, spearing formulas, consolidation tools,
pivot tools. ................................................................................................................................ 9
3. Benford’s Law – Benford’s Law predicts the occurrence of digits in large sets of data and these
predictions might help red-flag potential irregularities. ........................................................ 23
4. Using Regression to Create & Using Budgets to Detect and Prevent Fraud – An accurate
budget should be the CPAs’ first line of defense for detecting and preventing fraud. Attendees
will learn how to quickly create a budget using regression analysis applied to historical data.
You will also learn how to scrutinize each line item using a variety of techniques including
Pearson, R-Square, Skew and Kurtosis functions to determine if a suitable basis for regression
analysis exists, and if not, alternative methods will be used to budget that particular line item.
That budget will then be seasonalized and rounded. From there, a balance sheet budget and
cash flow forecast will be prepared based on the seasonalized budget, and the importance of
using a seasonalized budget to detect and prevent fraud will be discussed. ..................... 29
5. Profit Margin Monitoring – Profit margins that miss their target speak volumes. Attendees
will learn how to budget for profit margins by asking two simple questions and working
backwards using prior year data to target specific profit margins. Once established, these
profit margins can also be used as benchmarks to help detect fraud or errors. .................. 49
6. Proof of Cash - Many auditors use a four-column bank reconciliation, also known as a Proof
of Cash, to help shed light on error, misstatements, and fraud............................................. 54
7. Excel Data Cleaning – Attendees will learn how to clean data so that Excel’s tools can be
applied to analyze the data. For example, a general ledger will be exported to Excel and the
steps necessary to prepare the data for analysis will be explained. Attendees will learn how
to parse data using functions as well as the Text to Columns tool, and will learn when the
functions work better method for parsing data. .................................................................... 58
8. Data Cleaning Case Study - Preparing QuickBooks Data - When it comes to pivoting
QuickBooks data in Excel, you must first do a little bit of cleanup work before pivoting process
can begin.. ............................................................................................................................... 75
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9. Looking for Fraud – This section coves 28 common things to look for when examining for
fraud, and suggests various Excel tools that might help the fraud examiner conduct these
examinations. .......................................................................................................................... 80
10. Data Analysis Tools – Attendees will practice using data analysis tools to slice and dice data,
filter data, group data, subtotal data, and pivot data. These topics focus on the most
important aspect of Excel – the data tools – which can be specifically used to analyze data
and detect anomalies. ........................................................................................................... 85
11. Querying – Attendees will learn how to pull data directly from an accounting system, from
within Excel, for quick and easy data analysis. .................................................................... 111
12. Sparklines - Sparklines are new tools in Excel that can be used to visually analyze large
volumes of data, and attendees will learn how to utilize this tool to save time and provide
better data visualizations. ................................................................................................... 117
13. Conditional Formatting – Attendees will practice with Excel’s conditional formatting tools
which allows the user to create rules for highlighting data with different colors to help visually
analyze the data. .................................................................................................................. 117
14. Excel Functions – Attendees will also learn about a variety of tips and tricks such as Aggregate
function which can be a useful tool in analyzing data, and will receive a list of the top 171
functions the course instructor thinks apply to CPAs. ......................................................... 121
15. Ratio Reporting – Attendees will receive sample workbooks and templates containing sample
functions and ratio calculations. .............. Visit www.CarltonCollins.com, click the Fraud tab
16. Instructor Biography ............................................................................................................ 144
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Using Excel to Detect Fraud
CPE Course Information
Learning Objectives
Course Level
Pre-Requisites
Advanced Preparation
Presentation Method
Recommended CPE Credit
Handouts
Instructors
To increase the productivity of accountants and CPAs
using Excel’s commands and functions related to
possibly detecting fraud
Intermediate
Familiarity with Microsoft Excel
None
Live lecture using full color projection systems and live
Internet access with follow up course materials
8 hours
Templates, checklists, web examples, manual
J. Carlton Collins, CPA
AdvisorCPE is registered with the National Association of State Boards of
Accountancy (NASBA) as a sponsor of continuing professional education on
the National Registry of CPE Sponsors. State boards of accountancy have
final authority on the acceptance of individual courses for CPE credit.
Complaints regarding registered sponsors may be addressed to the national
Registry of CPE Sponsors, 150 Fourth Avenue, Nashville, TN, 37219-2417.
Telephone: 615-880-4200.
Copyright  April 2013, ASA Research and Accounting Software Advisor, LLC
4480 Missendell Lane, Norcross, Georgia 30092 770.842.5904
All rights reserved. No part of this publication may be reproduced or transmitted in any form without the express
written consent of ASA Research, subsidiaries of Accounting Software Advisor, LLC. Request may be e-mailed to
marylou@asaresearch.com or further information can be obtained by calling 770.842.5904 or by accessing the
ASAResearch home page at: http://www.ASAResearch.com/
All trade names and trademarks used in these materials are the property of their respective manufacturers and/or
owners. The use of trade names and trademarks used in these materials are not intended to convey endorsement
of any other affiliations with these materials. Any abbreviations used herein are solely for the reader’s convenience
and are not intended to compromise any trademarks. Some of the features discussed within this manual apply only
to certain versions of Excel, and from time to time, Microsoft might remove some functionality. Microsoft Excel is
known to contain numerous software bugs which may prevent the successful use of some features in some cases.
Accounting Software Advisor makes no representations or warranty with respect to the contents of these materials
and disclaims any implied warranties of merchantability of fitness for any particular use. The contents of these
materials are subject to change without notice.
Contact Information for J. Carlton Collins
Carlton@ASAResearch.com
www.Facebook.com/CarltonCollins
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Random Numbers
Excel provides two tools for generating random numbers as follows:
1. RAND
2. RANDBETWEEN (You must first activate the Analysis ToolPak)
RAND - The RAND function in Microsoft Excel allows you to generate random numbers in Excel.
Specifically, type RAND() in a given cell to produce a random number between 0 and 1, as shown.
Comments
1. RAND is a volatile function, which means it will be recalculated any time the enter key is
pressed, so the random number constantly changes. To prevent random numbers from
changing, most people copy and paste them as values.
2. Excel’s RAND function can be used to generate random numbers from the Uniform
distribution, however, be aware that prior to Excel 2003, this function should not be used
with large simulation models because the older versions of Excel use the generation
algorithm which has a relatively small period (less than 1 million numbers), so if your
model contains hundreds of variables and you are running the simulation tens of
thousands of times, you can run out of random numbers. This problem has been fixed in
Excel 2003 and later versions.
3. Note that there is a known bug in Excel 2003 causing the RAND function to return negative
numbers, which can be negated using the ABS (absolute function).
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RANDBETWEEN - In Excel 2010, 2007 and 2003, you must first activate Analysis Tool Pack add-in
as follows. In Excel 2010 and 2007, select File, Options, Add-ins, GO and place a check in the
check box labeled Data Analysis ToolPak, then click OK. In Excel 2003, select Tools, Add ins,…
The RANDBETWEEN returns a random integer between two specified numbers, as shown.
Suppose you wanted to select 100 random numbers from three different ranges of the
population. There are several approaches you might take. The first approach might be simply to
list all possible values, then use RAND to create a random number adjacent to those values, and
then sort. The screen shots below show the data before and after sorting. Once sorted, simply
take the desired number of samples from the top of the randomly sorted list, as suggested by the
top 7 values shaded in the second screen shot.
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This method takes up a lot of Excel screen real estate, but so what? Excel has millions of rows
and the generation of such a report is fairly straight forward and fast. Just use the FILL command
to fill in the necessary ranges, then add RAND and sort – it might take you 2 to 3 minutes.
Random Numbers Given Three Ranges of Population Data
Another way to generate random numbers is to use RANDBETWEEN, and assigning the
probability that a random number is selected from a given range based upon the percentage that
range represents compared to the total population. For example, consider the following
example:
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Cells C4 through E5 contain the low and high values for three separate ranges of data making up
the total population. We start by calculating the number of occurrences in each range (1,111,
1,277 and 55 in this example), and then calculate the percentage each range represents for the
total population (45%, 52% and 2% in this example).
Thereafter, a series of RANDBETWEEN functions are used to produce random number triggers
between 1% and 100% (contained in cells I3 through I22 in this example), and then an IF function
is used to calculate within which range each random number trigger falls. For example, the first
random number trigger is 49% (in cell I3), which falls within the second range data. The
RANDBETWEEN function in cell J3 thusly calculates a random number using the second range of
data’s low and high values (6,500 and 7,777 in this example), and the first random number
generated is 7,731 in this example.
This method could theoretically be used to calculate random numbers for many separate ranges
of data, with each member of the population having an equal chance to be selected.
Download this workbook at www.CarltonCollins.com/5random.xlsx.
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Consolidating Data in Excel
Consolidating data is a common task for CPAs, and Excel offers a variety of methods for
performing this task. The particular method you use will probably depend on the layout of your
data, and you may need to clean, edit or manipulate your data a bit to prepare it for
consolidation. CPAs often have a need to consolidate data such as budgets, months,
departments, locations, warehouses and sale representatives.
Following I will explain four different consolidation methods - two methods for consolidating data
with similar layouts, and two more methods for consolidating data with dis-similar layouts. These
four methods are as follows:
A.
B.
C.
D.
Simple formulas.
Spearing formulas.
The “Data Consolidate Command”.
The “PivotTable Wizard”.
A. Using Simple Formulas To Consolidate Similar Data
The workbook below contains budgets with identical layouts for Departments A, B, C and D.
The goal is to consolidate these four budgets into a single consolidated budget.
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1. Insert a New Worksheet on which the consolidation will appear
i. Don’t click the New Sheet or Add Sheet Option - Because there is a better
and quicker approach.
ii. CTRL + Drag Tab – To insert a new worksheet, select worksheet labeled
“Dept D”; then use the CTRL + Drag Tab keystroke combination to create a
duplicate worksheet of Dept D. The advantage is that the data, column
widths, page footers and headers, and margin settings are all duplicated
automatically, so you don’t have to create a new page from scratch.
iii. (The menu method for achieving this same procedure is to right click on
the tab and select Move or Copy, but CTRL + Drag Tab is quicker.)
iv. Clean the Page – Clean the new worksheet by deleting the data in the grid
area, so that new formulas can be inserted.
v. Re-label – Change the worksheet label in Cell A1 and on the Worksheet
Tab to read “consolidated”.
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2. Formula – In cell B5, enter a formula adding the B5 cells in the four budget sheets.
The formula should look like this:
='Dept A'!B5+'Dept B'!B5+'Dept C'!B5+'Dept D'!B5
3. Copy – Copy the formula down and across to complete the consolidation, and you
are done.
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B. Using Spearing Formulas To Consolidate Similar Data
The workbook below contains budgets with identical layouts for Departments A, B, C and D.
The goal is to consolidate these four budgets into a single consolidated budget.
1. Insert a New Worksheet on which the consolidation will appear
i. Don’t click the New Sheet or Add Sheet Option - Because there is a better
and quicker approach.
ii. CTRL + Drag Tab – To insert a new worksheet, select worksheet labeled
“Dept D”; then use the CTRL + Drag Tab keystroke combination to create a
duplicate worksheet of Dept D. The advantage is that the data, column
widths, page footers and headers, and margin settings are all duplicated
automatically, so you don’t have to create a new page from scratch.
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iii. (The menu method for achieving this same procedure is to right click on
the tab and select Move or Copy, but CTRL + Drag Tab is quicker.)
iv. Clean the Page – Clean the new worksheet by deleting the data in the grid
area, so that new formulas can be inserted.
v. Re-label – Change the worksheet label in Cell A1 and on the Worksheet
Tab to read “consolidated”.
2. Formula – In cell B5, enter a formula adding the B5 cells in the four budget sheets.
The formula should look like this:
=SUM('Dept A:Dept D'!C5)
I use the mouse to accomplish this step. Start by typing “=SUM(“, then click on cell B5 in
Dept A, hold the shift key down, and click cell B5 in Dept D.
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3. Copy – Copy the formula down and across to complete the consolidation, and you
are done.
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C. Using the “Data Consolidate Command”
To Consolidate Dissimilar Similar Data
The workbook below contains dis-similar budgets for Departments A, B, C and D.
Specifically, the four worksheets contain budgets for separate departments, and some
budgets contain more rows and different row descriptions than others. The goal is to
consolidate these four departmental budgets using the Consolidate Command.
1. Create A New Worksheet – Insert a new worksheet. Because there is no need to
duplicate a template from another worksheet you might be tempted to use the New
Sheet button this time; however, this approach does not duplicate the worksheet’s
page footers, headers or margin settings. Therefore I would still recommend that you
use the CTRL + Drag Tab method as described in examples 1 & 2 above to create a
new sheet, and I would then eliminate the data on the new sheet by deleting those
columns that contain data.
2. Label – As before, label the new worksheet in Cell A1 and on the Worksheet Tab to
read “Consolidated”.
3. Select Cell – Select a blank cell on the new worksheet such as B3.
4. Use the Consolidate Command - From the Data tab, select Consolidate to display the
Consolidate dialog box as pictured below. Click the Cell Chooser button, then highlight
the data only on Dept A, click Enter, and then click Add. Repeat this process for Dept
B, Dept C and Dept D.
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5. Check the check boxes to use Labels in the Top Row and Left Column.
6. Finish – Click OK to produce the results.
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7. Add Totals and Formatting - Highlight your data and expand the selection to include
a blank bottom row and blank right column. Click the AutoSum tool, add formatting
and you are done.
Comments:

Row Descriptions - Note that the consolidation works only to the extent that the
different worksheets contain the same row descriptions. If your four department
heads had used the descriptions: Rent, Rent Exp., Rent Expense, and Rental Expense,
then those rows would not actually be consolidated, rather they would be shown as
four separate rows in the resulting consolidation. However, because all four
department heads did use the phrase Rent to describe that row, the four respective
rent rows were properly consolidated.

Account Numbers – As an option, you might insert account numbers to the left of the
row descriptions to consolidate dissimilar information which contains dis-similar row
descriptions.

To Update – To update the results, place your cursor in the upper left hand corner of
the Consolidation range, and rerun the Consolidate command. If the resulting report
is a different size, you may need to clean up your data and reapply totals.
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
Consolidate Different Workbooks – Excel can also consolidate data from different
workbooks. The procedure is exactly the same except that you use the Browse button
instead of the Cell Chooser button to point to your data ranges.
The Problem with Data Consolidate
The problem with Data Consolidate occurs when you attempt to change the source data, for
example when you insert a new row in Dept A; a comedy of errors ensues as follows:
1. When you change the source data, the consolidated report does not update automatically.
2. Pressing the Refresh button does nothing.
3. To update the consolidated report, you must rerun the data consolidate command. But upon rerunning this command, you find that your cursor needs to be in the exact same location when
you ran it last time.
4. You also find that you need to erase the previous data before re-running the Data Consolidate
command.
5. Next you find that in Excel 2003, 2007 and 2010, you need to re-adjust your consolidating range
for Dept A because the tool did not automatically expand the selection when the row was
inserted in Dept A. (This issue has been corrected in Excel 2013.)
Because source data tends to change frequently, you are probably better off using the next
method to consolidate your data - the PivotTable and Chart Wizard method.
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D. Using A PivotTable To Consolidate Dissimilar Similar Data
The workbook below contains the same dis-similar budget data used in example 3 above. The four
worksheets contain budgets for separate departments, and some budgets contain more rows and
different row descriptions than others. The goal is to consolidate these four departmental budgets using
the PivotTable approach.
1. Create A New Worksheet – Start by inserting a new worksheet. (Because there is no need
to duplicate a template from another worksheet you might be tempted to use the New
Sheet button this time; however, this approach does not duplicate the worksheet’s page
footers, headers or margin settings. Therefore I would still recommend that you use the
CTRL + Drag Tab method as described in examples 1 & 2 above to create a new sheet, and
I would then eliminate the data on the new sheet by deleting those columns that contain
data.)
2. Label – Label the new worksheet in Cell A1 and on the Worksheet Tab to read
Consolidated.
3. Select Cell – Select a blank cell such as B3.
4. Add the PivotTable Wizard to Your Quick Access Toolbar – The PivotTable Wizard in Excel
2003 allows you to pivot multiple consolidation ranges, but for unknown reason this tool
is hidden in later versions of excel. Therefore, in Excel 2013, 2010 and 2007, you must first
customize your Quick Access toolbar and insert the icon titled PivotTable and PivotChart
Wizard as shown below.
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To add this tool, right-click your Quick-Access toolbar, select Customize Quick Access
Toolbar, select the option to View All Commands, locate the PivotTable and PivotChart
Wizard icon and add it to your toolbar. The resulting toolbar will appear as follows. .
5. Run the PivotTable Wizard – Click the PivotTable and PivotChart Wizard icon to display
the PivotTable and PivotChart Wizard dialog box as shown below. Choose the Multiple
consolidation ranges option and click Next, and Next again. The dialog box on the right
should now be displayed.
Click the Cell Chooser button, then highlight the data only on Dept A, click “Enter”, and
then click “Add”.
Repeat this process for Dept B, C and D until the PivotTable Wizard appears as follows.
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6. Finish – Click “FINISH” to produce the results.
7. Add Formatting - Highlight your data and add comma formatting with no decimals, adjust
the columns widths to taste, center the column headings; then you are done.
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Comments:
The PivotTable Wizard approach is superior to the Data Consolidate approach for many reasons as
follows:
1. Totals are automatic inserted with the PivotTable method.
2. Total column and row labels are automatically inserted with the PivotTable method.
3. AutoFilter buttons are automatic inserted with the PivotTable method.
4. If the source data changes, such as inserting a new row in Dept A, simply click refresh to update.
5. The resulting PivotTable is drillable.
6. The resulting PivotTable can be pivoted.
7. The PivotTable report offers many PivotTable tools such as PivotTable formatting which Data
Consolidate does not offer.
Download these Consolidation example templates at:
www.CarltonCollins.com/consolidatesimilarbudgets.xlsx
www.CarltonCollins.com/consolidatedissimilarbudgets.xlsx
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Benford’s Law
Benford’s Law predicts the occurrence of digits in large sets of data. Simply put, this law maintains
that we can expect some digits to occur more often than others. For example, the numeral 1
should occur as the first digit in any multiple-digit number about 30.1% of the time, while the
numeral 9 should occur as the first digit only 4.6% of the time. We also can apply the law to
determine the expected occurrence of the second digit of a number, the first two digits of a
number and other combinations.
How can such predictions help you red-flag potential irregularities? When someone creates false
transactions or commits a data-entry error, the resulting numbers often deviate from the law’s
expectations. This is true when someone creates random numbers or intentionally keeps certain
transactions below required authorization levels.
For example, in 2008, Bernie Madoff famously created fictitious data to hide an
estimated $65 billion in losses resulting from Madoff’s investment Ponzi scheme.
– Had the CPAs looked at this data using Benford’s Law, they might have found that
the digits smelled of fraud, perhaps triggering a deeper investigation.
Applying Benford’s Law Using Excel
According to Benford’s Law, the various digits should occur as the first digit position according to
the following percentages.
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To analyze data, simply use the LEFT function to extract the leading digits, and then add them up
as follows. As a simple example, I found a random workbook containing 136 rows of revenue
amounts. I entered a formula in cell C4 to extract the first digit and copied this formula down.
Next I used the COUNTIF function to count the number of occurrences of each of the nine digits,
and calculated their rate of occurrence, then charted the results.
You can clearly see that this data pattern does not conform to Benford’s law, and yes, I fabricated
this particular set of data years ago.
When Excel helps you spot a deviation like this, it raises a red flag. Considerable statistical
research supports the effectiveness of Benford’s Law, making it a valuable tool for CPAs. The
technique isn’t guaranteed to detect fraud in all situations but is useful in analyzing the credibility
of accounting records.
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A Note of Caution
Benford’s Law is not effective for all financial data. If the data set is small, the law becomes less
accurate because there are not enough items in the sample and so the rules of randomness don’t
apply—or at least apply with less predictability.
Also, if the data include built-in minimums and maximums, they also might not conform well to
the law’s predictions. For example, consider a petty-cash fund where all disbursements are
between a $10 minimum and a $20 maximum. All first digits would be either 1 or 2, and the
expected distribution of first digits would not apply. Likewise, when a company’s major product
sells for, say, $9.95, most sales totals will be a multiple of 995, again offsetting the value of the
process. Finally, when a data set consists of assigned numbers, such as a series of internally
generated invoice numbers, the data will not follow a Benford distribution.
History of Beford’s Law
Newcomb, 1881 - The discovery of Benford's law dates back to 1881, when the American
astronomer Simon Newcomb noticed that in logarithm tables (used at that time to perform
calculations) the earlier pages (which contained numbers that started with 1) were much more
worn than the other pages. Newcomb's published result is the first known instance of this
observation and includes a distribution on the second digit, as well. Newcomb proposed a law
that the probability of a single number N being the first digit of a number was equal to log(N + 1)
− log(N).
Benford, 1938 - The phenomenon was again noted in 1938 by the physicist Frank Benford, who
tested it on data from 20 different domains and was credited for it. His data set included the
surface areas of 335 rivers, the sizes of 3259 US populations, 104 physical constants, 1800
molecular weights, 5000 entries from a mathematical handbook, 308 numbers contained in an
issue of Readers' Digest, the street addresses of the first 342 persons listed in American Men of
Science and 418 death rates. The total number of observations used in the paper was 20,229.
This discovery was later named after Benford.
Varian, 1972 - In 1972, Hal Varian suggested that the law could be used to detect possible fraud
in lists of socio-economic data submitted in support of public planning decisions. Based on the
plausible assumption that people who make up figures tend to distribute their digits fairly
uniformly, a simple comparison of first-digit frequency distribution from the data with the
expected distribution according to Benford's law ought to show up any anomalous results.
Nigrini, 1999 - Following this idea, Mark Nigrini showed that Benford's law could be used in
forensic accounting and auditing as an indicator of accounting and expenses fraud. In practice,
applications of Benford's law for fraud detection routinely use more than the first digit.
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Analyzing All Digits
Building on the simple example described above, we can expand our Excel formulas to analyze
all of the digits included in a set of data as follows: In the example shown below, I have used the
MID function to extract each digit from the column of values in column A, and the resulting
individual numerals are displayed in columns C through H. Next, I totaled the occurrence for each
number 1 through 9 in the summary box and charted the results.
In this example, the data does appear to ever so slightly adhere to Benford’s Law as the first 4
bars in the chart and a few others seem to come close to matching Benford’s declining curve.
According to Benford’s Law one would expect lower numerals to appear more frequently than
higher values, but why? Lower digits (1, 2,& 3) tend to appear more frequently than higher digits
(7, 8, & 9) because it is easier to own 1 acre than 9 acres; and more people have $100 than $900.
Lower numbers are typically more achievable than higher numbers in many situations. For this
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reason, we would expect to see an analysis of numbers form a slightly curved declining chart like
this one:
However, the presence of the leading digit may significantly skew the data, therefore, we could
ignore the leading digit and analyze the occurrence of the remaining 8 numerals in an effort to
determine whether or not the data appears to roughly follow Benford’s Law. Ignoring the leading
digit reveals the following analysis.
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As expected, the numeral 1 occurs less often than when the leading digits were included. Still,
with the leading digit ignored, the data appears not to follow Benford’s Law.
Conclusion
This case study was intentionally brief and is only intended to convey the general ideas related
to Benford’s law. The forensic CPA may choose to run these numbers to help confirm suspicions
or beliefs related to the authenticity of a large data set of numbers in question. There are
probably many instances where this approach would offer little value, however in a high ticket
audit with high potential for fraud, running Benford’s Law analysis is a rather quick exercise which
may offer insights to help the forensic CPA determine how to best proceed.
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Using Regression to Create Budgets &
Using Budgets to Detect and Prevent Fraud
Excel provides the ability to extrapolate data from your accounting system to produce budgets,
projections or forecasts using the least squares method of linear regression. The process is
extremely easy as illustrated in the following example.
A Quick Example:
In this example I have exported the income statements for the past six years from my QuickBooks
accounting system. The next step is to highlight these five columns (from 2009 through 2013 as
shown below), and drag the Fill Handle to project 2014 beginning budget values. (Please note
that in this example I have selected the entire columns and the Fill Handle is shown in the upper
right hand corner of the selected range.)
Using the Fill Handle to Create a Budget for 2014 based on Five Years of Actual Data
Why Does This Work?
But why does this work? How can a simple drag of a mouse create a sophisticated budget? To
better understand the underlying workings of this concept, let’s start with a more simplified
example using simple regression.
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Simple Regression Example:
In the screen below we start with three columns of data for the months of January, February and
March.
Start with Three Simple Columns of Data
Simply highlight the three columns and drag the Fill Handle out an additional three columns. The
result is that Excel fills in new columns for April, May and June – including column headings,
column totals and forecast data, as pictured below.
The Fill Handle Uses Regression to Project April, May and June
Explaining Regression:
So where does this new data come from? The answer is that Excel uses linear regression to
produce this data. Excel evaluates the data for January, February, and March on a row by row
basis, and uses this information to project the subsequent variables. To help you better
understand this concept, here is how regression works from a visual perspective:
1. Once again, a simple example using Excel’s Fill handle. The 8 month’s of data yields a projected
value of 5,967.
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2. This time we use the same data, but instead of using the Fill handle, we use the SLOPE and
INTERCEPT functions to solve for month 9’s projected value.
As you can see above, the slope and intercept functions produce the exact same result as does
dragging the Fill Handle, thus proving that the math used by Excel is accurate.
Yet another way to produce the same results is to use the FORECAST function, as follows:
As you can see in this above example, the FORECAST function also produces the same result as
the Fill Handle and the SLOPE & INTERCEPT calculations.
All three of these forecast calculations, which produce the same identical values, can be viewed
visually by creating a Scatter Chart, and then applying a Trendline, as follows:
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The dotted trend line above is based on linear regression as described in the preceding
paragraphs. To forecast future values, Excel simply extends this trend line, and then uses the
intervals of the original data to plot future values, as suggested by the red dotted arrows below.
Now watch what happens when we base the trendline on logarithmic regression instead of linear
regression. In the chart below, we see that the trendline is now curving slightly.
Non-Linear Regression:
Excel provides 5 forms of non-linear regression (as shown in the Trendline Options box in the
image above) – Exponential, Logarithmic, Polynomial, Power and Moving Average. Collectively,
these 5 Trendline options are based on different forms of non-linear regression, which is
explained in detail on this Wikipedia page http://en.wikipedia.org/wiki/Nonlinear_regression.
The Wikipedia’s explanation is very complicated, but to simplify: non-linear calculations weight
the data points differently based on their position on the trendline (with linear regression all data
points are weighted the same). Some mathematicians and CPAs maintain that non-linear
methods produce more accurate results as more recent data points tend to be more relevant to
producing a trend than older data points.
You can calculate forecast values in Excel using the Exponential form of regression by using the
GROWTH function, as follows.
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Notice that the projected value for month 9 is 5,995.86 using Exponential regression, which in
this example which is 29.22 higher than the projected value based on linear regression.
The simplest way to forecast values using Exponential regression is to drag the Fill Handle while
holding down the right mouse button, then selecting Growth from the popup menu as pictured
below.
This action will fill in the 9th month with a forecast value based on exponential regression instead
of linear regression.
LINEST and TREND Functions
Although not used in this case study, you should be aware that Excel provides two additional
forecasting functions - LINEST and TREND. These functions basically forecast values using linear
regression exactly like the FORECAST function. The FORECAST and TREND functions are simpler
to use than LINEST, but the advantage of the LINEST function is that it can also be used as an
Array function to fill in values for a large range of data. Presented below is a simple example of
the LINEST function.
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Data Analysis ToolPak
To use the LINEST function most efficiently, you should first load Excel’s Analysis ToolPak, as
follows. From the File tab, select Options, Add-Ins. In the Manage box, select Excel Add-ins, then
click Go. In the Add-Ins dialog box, select the Analysis ToolPak check box, and then click OK. The
Data Analysis ToolPak will then appear in your Data Ribbon.
The Data Analysis ToolPak’s Regression analysis tool uses the LINEST function to perform more
complicated regression analysis which includes controlling the confidence levels and calculating
and plotting residuals. The screenshot below shows an example of the Analysis ToolPak’s
Regression tool along (shown in the dialog box) and an example of the output generated by this
tool beginning in column H. As you can see the output is very complicated, but the resulting
output can then be used to fine tune various regression calculations.
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Closer inspection of the ToolPak’s regression tool reveals options for setting the Constant to
Zero, adjusting the Confidence Level, and utilizing a combination of Residuals, Standardized
Residuals, Residual Plots, Line Fit Plots, and Normal Probability Plots.
These detailed aspects of regression are beyond the scope for our particular budgeting purposes,
but following are links for those that wish to delve further:
1. The 2002 report Using Dummy Variables in Regression by Hun Myoung Park of Indiana
University (www.iuj.ac.jp/faculty/kucc625/documents/dummy.pdf is a good place to
start for educating yourself about these variables.
2. This Wikipedia page titled Errors and residuals in statistics goes further in depth into
residuals. (http://en.wikipedia.org/wiki/Errors_and_residuals_in_statistics)
3. A 6-page Duke University report walking you through an example for using the Data
Analysis ToolPak’s Regression tool is available here (http://tinyurl.com/cueqap2).
Shortcomings with the Data Analysis ToolPak’s Regression Tool:
To be fair, I should point out that Excel’s ToolPak Regression tool has a number of shortcomings,
including:
1. Missing Functionality – Other regression tools offer hierarchical regression and case weighting,
but Excel’s tool does not.
2. Inadequate Diagnostic Charts - Several common diagnostic charts are not included in Excel (for
example, scatterplot charts, residuals against predicted values, and normality plot of the
residuals.) Charting typically goes hand-in-hand with forecasting to help visualize the results.
3. No Standardized Coefficients – Without a standardized coefficient, it may be difficult to interpret
your results.
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4. Inadequate Diagnostic Statistics – The lack of collinearity diagnostics makes it more difficult to
understand the forecast data model, although Excel’s PEARSON, RSQAURE and SKEW functions
could be used to aide in understanding.
Regression Warning
Regression only works when the underlying data follows a consistent trend. If revenue has grown
steadily for the past six years, then regression will likely project a reasonable value for year seven.
However if revenue has jumped all over the board for the past six years, then regression will likely
give you a worthless projection for year seven.
For example, consider that in the past five years gasoline prices jumped from $1.60 per
gallon to more than $4.00 per gallon. If you use regression to predict gasoline prices for
future years based on this prior increase, regression will likely predict gasoline prices in
the $10.00+ per gallon range – but let’s hope that such a prediction would be inaccurate
– right?
Testing Data’s Suitability for Regression Calculations
Therefore, you should always visit each line item in the projection and consider whether the
projected values make sense. Excel provides at least two good functions to help you accomplish
this task – PEARSON and RSQUARE. For example, in the screen shot below, I have calculated the
suitability of 5 different sets of data for regression, using both the PEARSON and R SQUARE
functions. The first data set on row three has a perfect trend and scores a 100% in both the
PEARSON and R SQUARE calculations. However, the data sets that follow are comprised of an
increasingly less perfect trend, and the declining PEARSON and R SQUARE scores reflect this
decline.
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For example, I might conclude that the first four sets of data were found to have a sufficient trend
as to provide a suitable basis for regression calculations but that the data set in row 15 does not.
You should establish your threshold and consistently stick to that threshold. In this case, I might
require a minimum 80% PEARSON score and 65% R Square score in order to justify reliance on
that data as a basis for regression forecasting.
Two More Statistical Measures
Two other Excel functions that might also be useful for analyzing the suitability of data for
regression include KURTOSIS and SKEW, which both measure the symmetry of data along a bell
curve. For example, data that is perfectly symmetrical will yield a SKEW score of 0 (zero). The
closer a data’s SKEW is to zero, the less suitable that data is for regression, because the data’s
trend is considered unreliable, be it trending upwards or downwards. The KURTOSIS works
similarly, although it’s scoring is different as it is designed to measure multiple peaks, whereas
the SKEW measures a single Peak.
Alternatives To Regression
If data is found to be inadequate for regression calculations, then other forecasting methods will
be necessary. For example, you might:
1. Inflation Forecasting - Forecast future amounts based on prior year amounts inflated for inflation,
increases in the consumer price index, or some other inflation factor.
2. Percentage Forecasting - Forecast future amounts as a percentage of another line item, such as
sales or payroll. For example, Cost of Goods Sold (COGS) might be forecast as 45% of forecast
Sales since historically, COGS does approximate that percentage amount. Or you might forecast
Fringe Benefits as 15% of Payroll since historically, Fringe Benefits do approximate that
percentage amount.
3. Best Guess Forecasting - You might come up with another forecast amount based on discussions
with department heads. For example, the training budget might be forecast much higher than
regression, inflation, or percentage methods because you know that since the new version of
Windows 8 and Office 2013 will be implemented, a significantly higher than normal amount of
training will be needed to bring everyone up to speed on those products.
Always Use Your Better Numbers When You Have Them
(This should be obvious to all, but I will say it anyway…) Of course some budget line items should
never be forecast using regression or other forecasting methods because they are known
amounts. For example, regression may suggest that rent expense might be $236,433.12 for
January 2014, but since I have signed a lease agreement, I know that rent expense will be exactly
$220,000 for January 2014, so that is the amount I will use. The same goes for known line items
such as depreciation expense, web-hosting expenses, interest payments on outstanding loans,
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and any other contractually known obligations. You would always use these more accurate
numbers instead of regression’s projected numbers.
Critical Key Point to Understand
The key point is that regression represents a starting point for many of the budget line items, but
not all budget line items. In all probability, a combination of forecasting methods will need to be
applied depending on each particular line item – regression should not be relied upon for all
forecast data.
Detailed Budget Example Using Regression
Starting with Dynamics GP
Now that we’ve discussed the various concepts related to regression, you are now ready to see
it in action. In this example, we will start by exporting 4 years’ worth of income statement data
from Dynamics GP to Microsoft Excel (virtually every accounting system on the planet enables
users to complete this step). In Dynamics GP, we start by printing a 36-month income statement
to the screen (as pictured below) and exporting it to Excel.
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Next in Excel, Regression Creates the Initial Budget
Once in Excel, to create the initial budget, select the 36 columns with numeric data, then left click
and drag the “Fill Handle” out twelve additional columns to create the 2014 budget, as suggested
below.
The result is that Excel uses linear regression analysis to predict the future values. Keep in mind
that this is just an initial starting point.
Overwrite those line items where you have better numbers
Once we have completed this process, we then insert better numbers on those line items where
we have better budget amounts. For example, the current lease agreement will provide the most
accurate amount to use for rent expense. We would use our depreciation schedule to provide
the most accurate amounts for depreciation expense. For interest expense, we would look to the
loan amortization schedule to prove these numbers (and so on). However for those numbers
where you have no better basis to use for budget preparation purposes, why not use linear
regression analysis to provide the answer?
To accomplish this task, it is best to use the split screen tool to split the screen into four areas so
you can easily see the row descriptions and column headings for the corresponding budget line
items you are working with. (Excel 2013 no longer provides split screen tools on the scroll bars
as did Excel 2003, 2007 and 2010 – you must click the Split Screen tool icon on the View tab and
then adjust the splits by dragging them). Now scroll each line item and ask yourself if you have a
more accurate basis for forecasting that line item, and if so, insert those more accurate values.
For example, I have inserted new depreciation values (highlighted in grey) in the screenshot
below.
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Document Your Budget Values
For each line item you change, you should document the basis for that budget line item with an
Excel comment, (or some other method such as an adjacent in-cell comment). For example, in
the screenshot below, I have inserted Comments next to each account description indicating the
line item’s forecasting basis. Comments are indicated by small red triangles in the upper right
corner of a cell and the comment is displayed whenever you hover over the red tick mark with
your mouse.
To print comments, select Page Setup from the Page Layout tab, and on the Sheet tab select At
end of the sheet from the Comments dropdown box, as pictured on the left below. Note that
the comments do not show up in Print Preview, but they do appear as a printed page at the end
of your print out; an example of which is pictured on the right below.
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Testing Data for Regression Suitability
Next we will test each line item’s data for regression suitability. This step will help us determine
which rows, if any, need to be forecast using a method other than regression. We start this
process by labeling a couple of blank columns Pearson and R Square, then enter the respective
formulas to test the 36 columns of data row–by–row, as shown below, on the left.
Notice that both the PEARSON and R SQUARE
formulas return percentage values that are both
negative and positive, which means the data is
trending upward or downward. Since we don’t care
which direction the data is trending, (we only care
that it scores high), we can edit the formulas to
include the ABSOLUTE function (ABS) which
changes all amounts to positive numbers, as
picture above on the right.
Now we can set our thresholds to minimum scores,
let’s say 50% (Pearson) and 40% (R Square) for
example, then apply conditional formatting to flush
out those line items that meet our stated criteria.
As pictured to the right, those line items in columns
BB and BC containing formatting are not suitable
for regression based on our stated criterion level,
and another forecasting method will need to be
used to forecast those amounts. For example, we
may simply use last year’s number inflated by the
consumer price index.
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Budget Totals
Now that we have generated regression amounts, and overwritten those amounts where we
have more accurate numbers and also those where regression is not suitable, we continue by
totaling the 12 months to produce the annual 2014 budget amounts, as pictured below.
The purpose of totaling the annual budget is so we can adjust the monthly budget for seasonality,
as discussed below.
Adjusting for Seasonality
Annual budget amounts are not very useful because they do not allow you to compare actual to
budgeted results on a monthly basis – you must produce monthly budget amounts. However,
simply dividing an annual budget by 12 to produce monthly amounts is not good enough because
many line items are typically seasonal. For example, actual revenue may be twice as high in some
months compared to other months, but comparing these seasonal sales amounts to a nonseasonal budget is virtually meaningless because you can’t tell whether you are on target, off
target, or by how much. Therefore, it is difficult to determine whether corrective measures are
needed on a month to month basis.
Seasonal budgets make a big difference. I believe one of the primary reasons companies fail to
properly analyze their budgets to actuals throughout the year is because their budgets are not
seasonal to begin with, and therefore such comparisons are virtually meaningless.
To add seasonality to your budget; simply spread the annual amount of each budget line item
across the 12 months based on the ratio of last’s year’s monthly amounts compared to last year’s
annual amount, as follows.
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Start by creating column headings for the seasonal budget, as pictured below.
Next, enter a formula using last year’s January value (as of January 2013) as a the numerator and
the SUM of all of 2013’s values as the denominator, and then multiplied times the 2014 annual
budget amount (=AD6/SUM($AD6:$AO6)*$BB6), as pictured below.
Notice in this formula I have used dollar signs to anchor the column references so that I may copy
the formula down and across to complete the seasonality adjustments.
Rounding & Formatting
It is rather senseless to produce budgets with pennies, or even dollars; I recommend rounding
the results by editing the seasonality formula. Edit the seasonality formula adding the ROUND
function in front of the formula and “-2” to the end of the formula to round to the nearest
hundredths, as pictured.
Now recopy this revised formula (overwriting the previous seasonally adjusted budget data) to
update the budget.
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Finally, select a formatted column (such as column BA in this example) and click the Format
Painter tool; then highlight the twelve months budget to apply the formatting, as suggested
below.
This Income Statement Budget Is Not Yet Completed
At this point, we have prepared a complete monthly budget using regression supplemented with
other forecasting methods, and this effort may be sufficient for your needs. However, please be
aware that this budget example was simplified in order to more easily convey Excel’s regression
tools and concepts. There is more to the process for those truly dedicated to creating the most
accurate budget possible – keep reading.
Forecasting Revenue
In the example above, for the purpose of explaining regression as simply as possible, I treated
the budgeting process for revenue exactly the same as the budgeting process for expenses, but
in reality budgeting revenue is usually a different process from budgeting expenses.
For established companies, many projected expenses can be reasonably determined using
regression, inflation, percentage of sales or best guess forecasting methods. However, revenue
is subject to far greater external factors such as competition, marketing, the state of the
economy, inflationary pressures, changing attitudes, etc. For example, the appearance of a new
competitor in the marketplace could steal away market share and thus negatively impact
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revenue. For example, in late 2012 Apple shares fell from $700 a share to almost $400 a share
for no other reason than the prospects that Microsoft’s, Google’s and Samsung’s new tablet PC
offerings were expected to eat into Apple’s market share.
Negative press related to the quality of your product (such as the gas pedal sticking for Toyotas)
could adversely affect sales. By contrast, your product may become wildly popular if a well know
celebrity starts wearing or using your product. A good marketing campaign can help significantly,
or hurt if it happens to make the wrong impression.
The point is that regression is unable to incorporate factors like this, therefore a more detailed
forecasting approach is usually needed. A good budget will consider all of the relevant factors
and in the end, you may produce multiple budgets given differing anticipated scenarios.
Simple Example of Revenue Projection Based on Units
In the following example, Crazy Fred’s has listed the number of training courses scheduled for
each month of the budget year, and has projected attendance for each month based on the
average attendance achieved in previous years for those same months. Crazy Fred charges a
course fee of $100 per attendee, which is input in cell A8. Crazy Fred also knows that the fixed
cost of printing the training manual and having the food catered will be $22 and $27, respectively
– as input into cells A11 and A12.
Notice that this projection method does is not based on historical revenue amounts, only
historical attendance figures have been used. In this example, the company knows how many
classroom venues have been booked and has a fairly decent idea as to what attendance might
be; therefore, regression based on historical revenue amounts would not be as accurate as using
these known quantities to forecast revenues.
A more sophisticated example of forecasting revenues based on units of production is shown
below. In this example, a CPA firm has listed each employee along with each employee’s
budgeted billable hours and billing rates by month.
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In this example, projected revenue is again based upon units rather than historical revenue
amounts, as regression methods applied to historical revenue amounts would likely yield less
accurate projections.
Keep in mind that revenue is often more volatile than expenses. An effective marketing program
might increase the number of units sold, a bad economy might adversely affect the number of
units sold. Any foreseen or expected events like these should be incorporated into the budget
and explained in detail.
In conclusion, while the regression example above was used to forecast both revenue and
expenses, in many cases regression should probably only be used as a means of forecasting
expenses only.
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Budgeting Balance Sheets and Cash Flow
In many cases, budgets consist of a profit and loss statement only, but I believe this falls short.
By creating a budgeted balance sheet and cash flow statement, (which requires the creation of a
budgeted balance sheet), a company can truly monitor expected results for every account,
including the all-important cash flow balance. The process starts by forecasting the balance sheet
and once created, forecasting cash flow is a simple matter of crunching the numbers.
To produce a budgeted balance sheet, assumptions are needed related to the days in accounts
receivable, accounts payable and inventory. These day calculations are best derived by examining
the historical days in accounts receivable, accounts payable and inventory for recent years, and
using those amounts as a guide. For example:
1. AR - The budgeted accounts receivable balance may be calculated as 46 days of the prior month’s
sales.
2. AP - The budgeted accounts payable balance may be calculated as 28 days of the prior month’s
variable expenses.
3. Inventory - The budgeted inventory balance may be calculated as 62 days of the prior month’s
COGS amount.
4. Loan Payments – Loan repayments should be budgeted based on the actual amortization
schedules, based on the principle payment amounts.
5. And so on.
Once the balance sheet items have been budgeted, the resulting cash flow budget is computed
as follows:
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The area in yellow (rows 5 through 19) shows the profit and loss budget as projected using the
methods described earlier above. The blue area (rows 21 through 25) depicts the assumptions
and the changes in balance sheet balances. The green areas (rows 26 through 32) represent the
forecast balance sheets and cash flow forecast. Because the income statement is seasonalized,
the balance sheet balances and cash flow forecast will also be seasonalized.
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Profit Margin Monitoring &
Calculating Your Desired Profit Margin
It is also useful for companies to budget and monitor their profit margins; a profit margin that
misses its target speaks volumes. Once established, budget to actual profit margin comparisons
can also be used as benchmarks to help detect fraud, errors or irregularities. To calculate your
desired profit margin, I suggest that you work backwards by asking yourself (or your client) two
simple questions, for example: Let’s assume that Burt has owned and operated a construction
company store for the past 17 years. As his CPA, I ask him two questions as follows: How much
profit do you want to make next year and how much sales do you anticipate next year?
Burt responds – “that’s easy, we’ve been growing at 8% a year for the past five
years and last year (2013) we nearly reached $12 million sales, so we will probably
hit $13 million in revenue next year (2014). Also, I’d like to make a million dollars
profit – I think that’s a reasonable goal.”
With just this little bit of data, we can work backwards based on Burt’s prior year financial
statements and advise him as follows:
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Burt’s fixed costs are a little more than $6 million in 2012, but let’s say that we can adjust
this amount down to $5,200,000 because the company was able to renegotiate and sign
a new lease agreement. The point is that we are using 2012’s fixed cost amount along
with any known adjustments. This allows us to work backwards to calculate the projected
Gross Margin of $6,200,000.
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From here we can compute Cost of Goods Sold, and then divide Cost of Goods Sold and
Gross Margin by Sales to derive the desired Profit Margin that will cover Fixed Costs,
Variable Costs and still have the desired Net Income of $1,000,000 left over. In
conclusion, a Profit Margin of 47.7%will yield the desired results.
Now that the optimum profit margin is known, let’s say that further analysis reveals that
the inventory and labor items on average are priced at just 44.5% above cost, as the
following calculations show, net income for 2014 would only be expected to reach
$585,000 – well below Burt’s desired profit.
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At this point, you need to convince Burt of the importance of pricing his products and
services at the desired profit margin in an effort to target the desired results. To convey
this point, you will tell Burt the following laughable story about the Florida boys who
started a business in Gainesville, Florida selling onions. It goes like this:
These two Florida boys were running up to Georgia and buying Vidalia onions at 4 for
$1.00 which they then took back to Gainesville and sold for a quarter a piece on the
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streets. The business was an instant success and soon those boys found themselves selling
from a road side stand, to a small store, to a much bigger store. The customers kept
coming and the business kept getting bigger. Soon they had customers lined up around
the block to buy those onions, which they kept buying 4 for a dollar and selling for 25
cents apiece.
After six months, one Florida boy turned to the other and said – “you know, business is
great! But I don’t think we’re making any money – what do you think we should do?” The
other Florida boy thought real hard and then blurted – “I think we need a bigger truck.”
OK, it’s an old exaggerated story, but there is a lesson to be learned here. If you don’t price your
products to make a profit, you will never make a profit. And, if you don’t price your products to
make your desired profit, you will never make your desired profits. In our example above, Burt
should consider setting his margin pricing to target a profit margin of 47.7%, instead of the
current profit margin of 44.5% to ensure a chance of achieving his desired goals. Without this
measure, Burt has absolutely no chance of reaching his goals, unless his revenue estimate is
wildly under-stated.
To be sure, if Burt’s costs go up or down, his prices will need to be adjusted accordingly to provide
the desired profit margin. But when you think about it, this approach is one in which Burt sells
his goods and services to his customers at the lowest price point possible that covers his fixed
costs, variable costs, and desired profit – and not a penny more. It seems reasonable that every
company in the world strive for this goal - right?
Here’s a simplified way to look at this - suppose your business was to purchase candy bars for
resell. Your only options are to sell the candy bars for:
A.
B.
C.
D.
E.
Below cost.
At cost.
At cost plus your desired profit.
At cost plus an egregiously high profit.
At cost plus some random profit that may or may not be sufficient.
I can’t see how any reasonable person could select any option other then C – yet I see many
companies sell their products based on all of these scenarios because they don’t take time to
calculate their desired profit margin, and then monitor that amount throughout the year.
You can download this Profit margin template at www.CarltonCollins.com/profitmargin.xlxs.
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Proof of Cash (Four-Column Bank Reconciliation)
Many auditors use a four-column bank reconciliation, also known as a Proof of Cash, to help shed
light on error, misstatements, and fraud. The proof of cash reconciles the bank balance and
general ledger over a specified period of time, whereas, the standard bank reconciliation
reconciles the two at a specific date. Download the monthly proof of cash template here:
www.CarltonCollins.com/5proof.xls.
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Purpose of Proof of Cash
The purpose of this report is to test cash transactions for a given period to verify the existence
and completeness assertions, as it relates to transactions. This report is frequently used when
internal controls over cash transactions are not effective and an audit of the ending cash balance
is not enough because of corollary misstatements to other accounts that result from unrecorded
cash transactions or fictitious transactions. This report specifically tests for the following four
items:
1. Tests that all recorded (on the client's books) cash receipts were actually deposited.
(existence)
2. Tests that all receipts deposited in the bank were recorded on the client's books.
(completeness)
3. Tests that all recorded cash disbursements were processed by the bank. (existence)
4. Tests that all disbursements processed by the bank were recorded. (completeness)
The standard 4 column proof of cash reconciles client books and records with 3 rd party bank
records for beginning (column 1) and ending (column 2) balances, as well as cash
receipts/deposits (column 2) and cash disbursements/charges (column 3) for a given period.
Usually one starts with amounts from the bank statement(s) and reconciles to what is reflected
in the client's general ledger and/or cash receipts and disbursements journal, as explained below:
Reconciling Item
Beginning Deposits in Transit
Column
Affected
1
Beginning Deposits in Transit
2
Ending Deposits in Transit
2
Ending Deposits in Transit
4
Beginning Outstanding Checks
1
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Explanation
You must add this amount to the beginning cash
balance per bank because the bank did not receive
the deposits before the prior month cut off.
You must subtract this amount from the deposits
shown by the bank for the period because these
were recorded on the client's books in prior period.
You must add this amount to the deposits shown
by the bank for the period because these were
recorded on the client's books this period, but will
not be received by the bank until next period.
You must add this amount to the ending balance
per the bank because the bank did not receive
them until after the end of the period, but they
were recorded on the client's books this period.
You must subtract this amount from the beginning
cash balance per bank because the bank did not
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Beginning Outstanding Checks
3
Ending Outstanding Checks
3
Ending Outstanding Checks
4
Customer NSF Checks redeposited by client in same
period
Customer NSF Checks redeposited by client in same
period
2
Customer NSF Checks redeposited by client in the
following period
3
Customer NSF Checks redeposited by client in the
following period
4
3
receive the checks for processing before the prior
month cut off.
You must subtract this amount from the
disbursements/charges shown by the bank for the
period because they were recorded on the client's
books in the prior period.
You must add this amount to the
disbursements/charges shown by the bank for the
period because they were recorded on the client's
books in this period, but will not be received by the
bank for processing until next period.
You must subtract this amount from the ending
cash balance per bank because the bank did not
receive the checks for processing until after this
month's cut off, but have been recorded on the
client's books.
You must subtract this amount from deposits per
bank because the client did not record the second
deposit as an additional receipt.
You must subtract this amount from
disbursements/charges per bank because the
return of the NSF check was not recorded on the
client's books as a cash disbursement.
You must subtract this amount from
disbursements/charges per bank because the
return of the NSF check was not recorded on the
client's books as a cash disbursement.
You must add this amount to the ending balance
per the bank because
 the bank reduced the balance when the check
was returned NSF by the customer's bank and
the client did not record it as an additional
disbursement and
 it is basically a DIT at period's end.
To Prepare a Proof of Cash:
1. Start with a beginning balance, typically a year-end balanced previously reconciled.
2. Reconcile receipts
3. Reconcile disbursements.
4. Complete it with the ending balance, typically the current year-end.
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The actual completion of this reconciliation can be relatively complex and time consuming.
Comments About the Proof of Cash Template:
1. Hints are included in comments for each cell (see red triangles in the template)
2. Check figures - each column must foot and cross foot (meaning the columns must
balance as well as the rows). Items that don't add up correctly are automatically
highlighted.
3. The dates entered at the top of the reconciliation automatically populate the rest of the
reconciliation.
4. An example reconciliation has been included on the second worksheet.
Tick marks are included to help document your work.
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Excel Data Cleaning
CPAs often receive or retrieve data from many sources in a wide variety of formats such as Text,
Comma-Separated-Value (CSV), or Web Page formats. You often cannot have control over the
format and type of data that you import. However, before you can analyze the data in Excel, you
may need to clean it up first. Fortunately, Office Excel has many features to help you clean data
and prepare it for analysis.
By data cleaning, you may think I mean reformatting the data so that the information is more
readable and font and font sizes are appropriate, but this is not the kind of data cleaning I am
talking about. What I am referring to is the need to separate data in one cell to multiple cells, to
repeat row descriptions where needed; add column headings, spell check for spelling errors,
remove duplicate rows, remove trailing spaces, eliminate leading zeros, etc. Some of Excel’s top
functions and commands for cleaning data are as follows:
1. Spell checker
2. Import
3. Text to Columns
4. Remove Duplicates
5. Find & Replace
6. Spell Check
7. =UPPER
8. =LOWER
9. =PROPER
10. =FIND
11. =SEARCH
12. =LEN
13. =SUBSTITUTE
14. =REPLACE
15. =LEFT
16. =MID
17. =RIGHT
18. =VALUE
19. =CONCATENATE
20. =TEXT
21. =TRIM
22. =CLEAN
23. =FIXED
24. =DOLLAR
25. =CODE
26. Macro
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Data Cleaning Strategies
1. Importing Data into Excel – Of course Excel opens Excel files, but what happens when
you attempt to open other file formats? The answer is that Excel attempts to
automatically import that data on the fly and displays an Import Wizard to help you
complete the process. The Text Import Wizard examines the text file that you are
importing and helps you import the data the way that you want. To start the Text Import
Wizard, on the Data tab, in the Get External Data group, click From Text. Then, in the
Import Text File dialog box, double-click the text file that you want to import. The
following dialog box will be displayed:
If items in the text file are separated by tabs, colons, semicolons, spaces, or other
characters, select Delimited. If all of the items in each column are the same length, select
Fixed width. In step 3, click the Advanced button to specify that one or more numeric
values may contain a trailing minus sign. Also click the desired data format for each
column to be imported.
Using Excel to Detect Fraud
2. Text to Columns – The Text to Columns command located on the Data Ribbon works exactly the
same way as described above – the user simply launches it to convert data within an existing
worksheet.
3. No Built-in Logic Checking - Excel’s Text to Columns tool does not automatically recognize
delimiters (commas, spaces, or quotes), although it may sometimes appear so. Like an elephant,
Excel’s Text to Columns simply has a good memory. Each time you use Text to Columns, it
remembers your parsing criteria and sets it as the default setting for future parse jobs, until Excel
is closed.
Some CPAs use the Text to Columns tool, changing the delimiter criteria as necessary, then they
open a second file containing the same type of delimited data. In this second case, it may appear
to them that Excel automatically recognized the embedded delimiter, but it was only following
the lead from the first parsing job.
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Note: There is no option for changing the default Text to Columns delimiter in Excel, but you can
achieve the same effect by setting the desired delimiters in a workbook and saving it as a template
or as the default workbook. You could also create macros designed to parse data according to the
delimiting criteria you frequently encounter.
4. Removing Duplicate Rows - Duplicate rows are a common problem when you import data. You
can identify and remove duplicate rows by using the Data, Advanced Filter, Unique Records Only
tool as show in the screen below.
5. Find and Replace Text – This tool can be used to identify and remove leading strings, such as a
label followed by a colon and space, or a suffix, such as a parenthetic phrase at the end of the
string that is obsolete or unnecessary. You can do this by finding instances of that text and then
replacing it with no text or other text.
Noteworthy Find and Replace Points:
1.
2.
3.
4.
You can find and replace for an entire worksheet, or the entire workbook.
You can find and replace formats with new formats.
There is a cell chooser option that makes it easier to find and replace formats.
If you highlight a range of cells, then Find and Replace only finds and replaces
within that range of cells.
5. You can replace all at once or one at a time.
6. You could also find and replace references in a formula.
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7. Searching for a “[“ is the only way to locate external links.
6. Find and Replace in Word, rather than Excel – In some cases, it may make more sense to first
paste your data into Word, and then use the Find and Replace tool in that environment, rather
than Excel. This is particularly useful when you are working with row data you want organized into
paragraphs.
For example, the easiest way to remove unwanted line breaks is to simply press Alt + Ctrl
+ K. This keystroke combination runs Word’s AutoFormat command which analyzes the
document and instantly applies an appropriate format, which includes the removal of
unnecessary line breaks. (In this case, you should consider adding the AutoFormat tool
to your Quick Access Toolbar for easy access.)
However, depending on the document, AutoFormat may delete (or alter) formatting you
wanted to keep. In this case, you are on the right track using the search and replace
method as you described, you just need to add two small steps to your procedure to
obtain the desired results. If you look closely, you will see that your document contains
single paragraph breaks where you don’t want them, and double paragraph breaks where
you do. The trick is to get rid of the breaks you don’t want but keep the breaks you do.
Start by replacing the double paragraph breaks (which you do want to keep) with an
uncommon sting of text that does not appear in the document, such as “5555”, as follows.
From the Office 2010 or 2007 Home tab, select Editing, Replace to launch the Find and
Replace dialog box. From the Office 2003 menu select Edit, Replace to launch the Find
and Replace dialog box. Then, in the Find what box, type ^p^p, and in the Replace with
box type 5555, as shown.
Click the Replace All button. Next, use find and replace again to remove all single
paragraph breaks (which you don’t want) as follows: From the Home tab, select Editing,
Replace and type ^p in the Find what box and enter a space in the Replace with box, then
click the Replace All button (this action will remove all paragraph breaks from your
document and adds spaces instead). Finally, restore the double paragraph breaks as
follows: From the Home tab, select Editing, Replace and type 5555 in the Find what box
and ^p^p in the Replace with box, then click the Replace All button. Your resulting
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document will be devoid of the unwanted paragraph breaks and you will be able to copy,
paste and edit the text unencumbered by unwanted line breaks.
7. Spell Check - You can use a spell checker in Excel to not only find misspelled words, but to find
values that are not used consistently, such as product or company names, by adding those values
to a custom dictionary. The spell check function also checks your grammar as well.
8. Changing The Case Of Text – You can use one or more of the three Case functions to convert text
to lowercase letters, such as e-mail addresses; uppercase letters, such as product codes; or proper
case, such as names or book titles.
Using a specific case in Excel is not necessary because Excel ignores case for all function
calculations (such as VLOOKUPS); however, you may want to convert case for consistency and for
better visual appeal and readability.
Excel does utilize case when performing a Search when the Match Case checkbox is selected, or
when performing a Sort when the Case Sensitive checkbox is selected.
a. = UPPER - Converts text to uppercase letters.
b. =LOWER - Converts all uppercase letters in a text string to lowercase letters.
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c. =PROPER - Capitalizes the first letter in a text string and any other letters in text that follow
any character other than a letter. Converts all other letters to lowercase letters.
9. Merging And Splitting Columns - A common task after importing data from an external data
source is to either merge two or more columns into one, or split one column into two or more
columns. For example, you may want to split a column that contains a full name into a first and
last name. Or, you may want to split a column that contains an address field into separate street,
city, region, and postal code columns. The reverse may also be true. Presented below are
functions that to help you accomplish these tasks:
10. =FIND – Returns the starting position of a character, string of characters or word with a cell.
Find is case sensitive.
11. =SEARCH – Returns the starting position of a character, string of characters or word with a cell.
Search is not case sensitive.
12. =LEN – Displays the length or number of characters in a cell.
13. =SUBSTITUTE – Replaces a character or characters with a character or characters that you specify.
14. =REPLACE - Replaces a character positions with a character or characters that you specify.
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15. =LEFT – Extracts the specified number of characters from a cell, starting from the left.
16. =MID – Extracts the specified number of characters from a cell, starting from somewhere in the
middle of the cell.
17. =RIGHT – Extracts the specified number of characters from a cell, starting from the right.
18. =Value – Converts text to values so the data can be added, subtracted, multiplied, divided or
referenced in a function.
19. =CONCATENATE - Joins two or more text strings into one text string.
Just to make you aware, Excel provides the following variations of these functions for use
when working with foreign languages or foreign characters like these ( ","
)
=FINDB; =SEARCHB; =REPLACEB; =LEFTB; =RIGHTB; =LENB; and =MIDB.
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Cleaning Text – (Removing Spaces And Nonprinting Characters From Text) - Sometimes text
values contain leading, trailing, or multiple embedded space characters ( character set values
32 and 160), or nonprinting characters (Unicode character set values 0 to 31, 127, 129, 141,
143, 144, and 157). These characters can sometimes cause unexpected results when you sort,
filter, or search. For example, in the external data source, users may make typographical
errors by inadvertently adding extra space characters, or imported text data from external
sources may contain nonprinting characters that are embedded in the text. Because these
characters are not easily noticed, the unexpected results may be difficult to understand.
Following is a list of functions you can use to remove these unwanted characters:
20. =TEXT - Converts a value to text in a specific number format.
21. =TRIM - Removes the 7-bit ASCII space character (value 32) from text.
22. =CLEAN - Removes the first 32 nonprinting characters in the 7-bit ASCII code (values 0 through
31) from text.
23. =FIXED - Rounds a number to the specified number of decimals, formats the number in decimal
format by using a period and commas, and returns the result.
24. =DOLLAR - Converts a number to text format and applies a currency symbol.
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25. =CODE - Returns a numeric code for the first character in a text string.
Fixing Dates and Times - There are many different date formats, and these varied formats
may be confused with numbered part codes or other strings that contain slash marks or
hyphens. Dates and times often need to be converted and reformatted. Presented below is a
list of functions that help you accomplish this task.
26. =DATE - Returns the sequential serial number that represents a particular date. If the cell format
was set to General before the function was entered, the result is formatted as a date.
27. =DATEVALUE - Converts a date represented by text to a serial number.
28. =TIME - Returns the decimal number for a particular time. If the cell format was set to General
before the function was entered, the result is formatted as a date.
29. =TIMEVALUE - Returns the decimal number of the time represented by a text string. The decimal
number is a value ranging from 0 (zero) to 0.99999999, representing the times from 0:00:00
(12:00:00 AM) to 23:59:59 (11:59:59 P.M.).
Transforming And Rearranging Columns And Rows - Most of the analysis and formatting
features in Office Excel assume that the data exists in a single, flat two-dimensional table.
Sometimes you may want to make the rows become columns, and the columns become
rows. At other times, data is not even structured in a tabular format, and you need a way
to transform the data from a nontabular to a tabular format. The following function can
help you achieve this goal:
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30. =TRANSPOSE - Returns a vertical range of cells as a horizontal range, or vice versa.
31. Data Fill In Trick – A clever trick for filling in missing data can be accomplished using the GOTO,
Special, Blanks command. Here is how it works. This trick works well when you have a large
volume of data but descriptions are not provided for every row, as shown in the example below:
Start by entering a simple formula referencing the data label in the above cell, just like
this:
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a.
b.
c.
d.
e.
f.
g.
Next copy that formula...
Highlight the entire range containing data labels in columns A and B. columns...
Press the F5 key to launch the GoTo dialog box...
Select the Options Box...
Click on the “Blanks” radio button...
Press Enter...
Paste.
This action will cause all data labels to repeat in the empty cells beneath. Next:
h. Copy columns A & B...
i. Paste Special as values to convert the formulas to text based data labels...
j. You are now ready to sort, filter, subtotal and pivot your data.
Fetching Data - Occasionally, database administrators use Office Excel to find and correct
matching errors when two or more tables are joined. This might involve reconciling two
tables from different worksheets, for example, to see all records in both tables or to
compare tables and find rows that don't match.
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32. =VLOOKUP - Searches for a value in the first column of a table array and returns a value in the same row
from another column in the table array. For example, consider the example below which uses a =VLOOKUP
function to calculate the appropriate amount of tax due based on the IRS rate schedule.
As the Income statement shown in the shaded area is updated, the resulting taxable
income amount is referenced in Cell F13. Next, 3 VLOOKUP functions pull the appropriate
rate, base and threshold information from the rate schedule to be used in calculating
income tax. Once calculated, the resulting tax is referenced back to the income
statement for the purposes of computing Net income after taxes.
Key points to Consider when Using VLOOKUP:
a. If you are looking up based on text, the first column containing lookup values must
be sorted alphabetically in descending order – else it will not work properly.
b. Another approach is to add the FALSE attribute at the end of the VLOOKUP function,
to force Excel to lookup values based on exact matches.
c. If you are looking up based on text, you must have an exact match between the
lookup value and the table array value.
d. If you are looking up based on values, the first column containing lookup values must
be sorted numerically in descending order – else it will not work properly.
e. If you are looking up based on values, then Excel will choose the closest value without
going over. For example, if the lookup value is 198,000 and the table array contains
values of 100,000 and 200,000, then Excel will choose 100,000 because 200,000 goes
over or exceeds 198,000. (It might be helpful to think back to the old Bob Barker game
show the Price is Right.)
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33. =HLOOKUP - Searches for a value in the top row of a table or an array of values, and then
returns a value in the same column from a row you specify in the table or array.
34. =INDEX - Returns a value or the reference to a value from within a table or range. There
are two forms of the INDEX function: the array form and the reference form.
35. =MATCH - Returns the relative position of an item in an array that matches a specified
value in a specified order. Use MATCH instead of one of the LOOKUP functions when you
need the position of an item in a range instead of the item itself.
36. =OFFSET - Returns a reference to a range that is a specified number of rows and columns
from a cell or range of cells. The reference that is returned can be a single cell or a range
of cells. You can specify the number of rows and the number of columns to be returned.
37. Data Cleaning with Macros - To periodically clean the same data source, consider
recording a macro or writing code to automate the entire process. There are also a
number of external add-ins written by third-party vendors, listed in the Third-party
providers section, that you can consider using if you don't have the time or resources to
automate the process on your own.
38. RAND( ), RANDBETWEEN( ), ROUND( ) – In Excel 2003, RANDBETWEEN is not in the
standard EXCEL 2003 installation but if the analysis tool pack is installed and the add-in
activated it is an extremely useful function.
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39. Informational Functions
CELL(info_type,reference) - Info_type is a text value that specifies what type of cell
information you want. The following list shows the possible values of info_type and the
corresponding results.
Info_type
Returns
"address"
Reference of the first cell in reference, as text.
"col"
Column number of the cell in reference.
"color"
1 if the cell is formatted in color for negative values; otherwise returns
0 (zero).
"contents"
Value of the upper-left cell in reference; not a formula.
"filename"
Filename (including full path) of the file that contains reference, as text.
Returns empty text ("") if the worksheet that contains reference has not
yet been saved.
"format"
Text value corresponding to the number format of the cell. The text
values for the various formats are shown in the following table. Returns
"-" at the end of the text value if the cell is formatted in color for
negative values. Returns "()" at the end of the text value if the cell is
formatted with parentheses for positive or all values.
"parentheses" 1 if the cell is formatted with parentheses for positive or all values;
otherwise returns 0.
"prefix"
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Text value corresponding to the "label prefix" of the cell. Returns single
quotation mark (') if the cell contains left-aligned text, double quotation
mark (") if the cell contains right-aligned text, caret (^) if the cell contains
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centered text, backslash (\) if the cell contains fill-aligned text, and
empty text ("") if the cell contains anything else.
"protect"
0 if the cell is not locked, and 1 if the cell is locked.
"row"
Row number of the cell in reference.
"type"
Text value corresponding to the type of data in the cell. Returns "b" for
blank if the cell is empty, "l" for label if the cell contains a text constant,
and "v" for value if the cell contains anything else.
"width"
Column width of the cell rounded off to an integer. Each unit of column
width is equal to the width of one character in the default font size.
Reference the cell that you want information about. If omitted, information specified in
info_type is returned for the last cell that was changed. The following list describes the
text values CELL returns when info_type is "format", and reference is a cell formatted with
a built-in number format.
If the Microsoft Excel format is
CELL returns
General
"G"
0
"F0"
#,##0
",0"
0.00
"F2"
#,##0.00
",2"
$#,##0_);($#,##0)
"C0"
$#,##0_);[Red]($#,##0)
"C0-"
$#,##0.00_);($#,##0.00)
"C2"
$#,##0.00_);[Red]($#,##0.00)
"C2-"
0%
"P0"
0.00%
"P2"
0.00E+00
"S2"
# ?/? or # ??/??
"G"
m/d/yy or m/d/yy h:mm or mm/dd/yy
"D4"
d-mmm-yy or dd-mmm-yy
"D1"
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d-mmm or dd-mmm
"D2"
mmm-yy
"D3"
mm/dd
"D5"
h:mm AM/PM
"D7"
h:mm:ss AM/PM
"D6"
h:mm
"D9"
h:mm:ss
"D8"
If the info type argument in the CELL formula is "format", and if the cell is formatted later
with a custom format, then you must recalculate the worksheet to update the CELL
formula.
Third-Party Solutions – In case Excel’s built in functions are not sufficient to meet your
needs, following is a partial list of third-party providers that have products that are used
to clean data in a variety of ways.
Provider
Product
Add-in Express Ltd.
Add-Ins.com
AddinTools
CDX
Click 2 Convert
DigDB
JKP Application Development
J-Walk & Associates, Inc.
Office Assistance LLC
PATools
PDF2XL
Spinnaker Software Solutions
Vonnix
WinPure
ListCleaner Pro
Clean and Match 2007
Advanced Find & Replace, Merge Cells Wizard
Duplicate Finder
AddinTools Assist
Zip Stream
Converts PDF to Excel formats
Add-ins for Excel®
Flexfind for Excel
Power Utility Pak Version 7
Similar Data Finder for Excel®
PATools Advanced Find Replace
Converts PDF files to Excel Formats
Spinnaker DB tools for Excel
Excel Power Expander 4.6
ListCleaner Lite
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Data Cleaning Case Study - Preparing QuickBooks Data
When it comes to pivoting QuickBooks data in Excel, you must first do a little bit of clean up work
before pivoting process can begin. In the following example, I have exported a QuickBooks
general ledger report from 1995 to 2011, and I walk you through the process of preparing the
expense portion of this data for pivoting, as follows.
1. Remove Empty Columns – QuickBooks provides an option for exporting a general ledge report to
Excel without empty columns, but this is not the default action. If you have not adjusted this default
setting, then your exported general ledger report will contain empty columns, and you should remove
them by selecting an empty column, right clicking on that cloumn, and selecting Delete Column.
Thereafter, you can select each subsequent empty column and press the F4 key to repeat the deletion.
2. Reformat Text Columns – QuickBooks does a nasty thing when it exports data to Excel in that it
formats all text columns with text formatting, making it impossible to insert text based formulas,
(which we want to do in the next step). To correct this problem, select the text columns and from the
Home tab, select General from the Number Format drop down options box in the Number group.
3. Delete the Non Expense Related Rows – In this example, the expense related rows begin on row
9490, therefore I will delete everything above that row, except the top row column headings. Select
these rows by clicking on row 9408 (not visible in screen shot), then hold the Shift Key down and press
HOME, then move down one row, right-click on the selected range and select DELETE. The data should
now appear as follows:
4. Insert Column Descriptions – In this example, there is no column description in Columns A & B,
therefore we need to insert descriptions – any description will do.
5. Repeat Account Description – In column B, the Account Description must be repeated on all
subsequent blank rows below. For example, the phrase Advertising – Business shown on 2 in the
screen image above needs to be repeated on rows 3, 4, 5, and 6. To do this:
a. In cell B3, enter the formula =B2.
b. Copy cell B3 by pressing Ctrl + C.
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c. Highlight the range from B2 to the end of column B’s data range, B8816 in this
example.
d. Press the F5 key to launch the GoTo dialog box.
e. Click the Special button, check the radio button labeled Blanks, then click OK.
f. Paste the data by pressing Ctrl + V.
g. Next, select Column B and copy the entire column by pressing Ctrl + C.
h. Select Home tab, Paste Special, Paste Values. (Note that without this extra step, you
will end up with formulas, and not values throughout column B, which will not be
suitable for Pivoting.)
6. Save Your Work – At this point, save your work (and save it often), perhaps to a new file name (such
as Export 2), in case power goes out or a major mistake is made.
7. Number Each Row – Insert a new column in front of Column A, and number them sequentially. To
insert numbers quickly, enter the number 1 in cell A1 and the number 2 in cell A2. Now highlight cells
A1:A2, and doubleclick the Fill handle. This action will automatically number your rows dow to row
8,816.
8. Eliminate Non Transaction Rows – The data’s transaction rows each contain a date, therefore sort
the data by the date column, then delete all rows that do not have dates, then resort the data by
column A in descending order.
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9. Delete Columns A & B – Delete columns A & B As they are no longer needed.
10. Format the Data – To make the data visually easier to read, format the data to your desired
appearance. For example, I changed all fonts to Calibri and all font colors to black; I removed
unneccessaery borders and lines; I bolded the column heading labels only; I adjusted column widths;
and I centered certain text columns. My data now appears as follows:
11. Tidy the Debits and Credits Columns – The presentation of the debits and credits columns are not
conducive to easy pivoting, therefore I inserted the formula to combine debits and credits in the same
column as shown below.
Next I converted the formulas contained in Column K to values using the same steps mentioned above
(in steps 5g & 5h), labeled Column K, then deleted Columns H, I and J as they are no longer needed.
My data now appears as follows:
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12. Data Review and More Cleaning – Now that the data has been cleaned from a layout point of view,
it still must be reviewed for consistency in content as well. For example, in this data set we can see
many problems as follows:
a. Consistency – A quick review reveals that consistent account descriptins and memos have not
been used throughout the data. For example, as shown below the phrase AT&T Universal Card
has not been entered consistently. If you plan to summarize data using this phrase, then
consistent phrasology is needed throughout.
b. No Amounts - Some transactions have no amounts, which may be due to Voided transactions or
other explanations. Each of these transactions should be reviewed to determine if that line item
should be removed completely.
c. Credit Amounts – Some transactions have credit balances, which aren’t expenses at all. Each of
these transactions should be reviewed to ensure the data is a valid expense, and if not,
consideration should be given to removing the transaction completely.
13. Ready for Pivoting – At this point, the data is ready for pivoting. As an example, the PivotTable report
shown below was created in only a few seconds, including time to adjust the grouping of the dates
listed across the report in Years.
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Looking for Fraud
Looking for Fraud in Accounts Payable
According to a report to the Nation on Occupational Fraud and Abuse by the Association of
Certified Fraud Examiners, losses to fraud in the average company amount to a staggering 6
percent of gross sales. Roughly 45 percent of all fraud involves asset misappropriations of cash
in an accounts payable related transaction with an additional 13 percent related to bribery or
corruption. This means that 58 percent of 6 percent, or as much as 3.5 percent of gross sales,
are lost in this one functional area. Many companies struggle along with profit rates much lower
than 3.5 percent! Furthermore, a majority of these misappropriations represent fraudulent
vendors, check tampering, and fraudulent expense reimbursements—items that proper controls
ought to deal with. It has also been found that companies completing internal or external fraud
examinations can reduce their median losses from $153,000 to $87,000 or over 40 percent.
Based on these statistics, if a company earns $250 million in sales, they lose an average of 6
percent to fraud from all causes ($15 million) of which roughly 45 percent involves
misappropriation of accounts payable ($6.8 million). Of this sum, 40 percent might be saved
through internal auditing ($2.7 million). Bottom line, if you can clean up fraud within your
accounts payable area, you can make an enormous difference.
1. Above Average Payments to a Vendor - Calculate vendor invoice averages, to locate all
invoice amounts exceeding more than twice the vendor’s average. Then scrutinize all
vendor payments that exceed twice the average. Use the AVERAGE function and then
conditional formatting.
2. Employee to Vendor Address Match - Compare the street address for vendors and
employees to see if there are any matches. Look for exact matches and then look for a
partial match. Use the IF, MATCH or VLOOKUP functions, along with the LEFT, MID or
RIGHT functions.
3. Duplicate Payments - Duplicate payments are sometimes converted to the use of an
employee. The employee may notice the duplicate payment, then he or she may prepare
a phony endorsement of the check.
Looking for Fraud in Revenue
Accounts receivable, and the revenue streams that drive it, is arguably the most risky part of a
financial statement audit. Any restatement, due to error or fraud, has the potential to bankrupt
an organization. According to one study, cash misappropriation fraud (when people who are
entrusted to manage an organization’s assets steal those assets) accounts for 30% of all fraud.
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Management is under pressure every day to meet sales and associated income targets. Stock
price, management compensation, and analyst/media commitments exacerbate this pressure,
so much so that it can persuade management to misstate earnings. Although such misstatements
may start as a laissez-faire reading of Generally Accepted Accounting Principles, they tend to
snowball into larger and larger entries until they become outright fraud.
A 10-year study commissioned by the Committee of Sponsoring Organizations of the Treadway
Commission concluded that more than half of frauds involved overstating revenue. This is
corroborated by the Association of Certified Fraud Examiner’s report to the Nation on
Occupational Fraud and Abuse. If the business model is sound and industry conditions do not
pose a threat, there is nothing for management to feel pressured about. Unfortunately, business
models are not realized as they are envisioned and the industry may be highly impacted due to
change and competition. Management will be the primary identifiers of these trends but may
not be compelled to explain such vulnerabilities with investors or creditors. Thus, it is imperative
for the fraud examiner to not only understand the business and industry conditions, but also to
utilize computer assisted tools to identify trends underlying the financial reports.
Aside from financial statement fraud explained above, there are three major types of fraud in the
accounts receivable/revenue area:

Skimming, in which cash is stolen from an organization before it is recorded on the
organization’s books and records.

Cash larceny, in which cash is stolen from an organization after it has been recorded on
the organization’s books and records.

Fraudulent shipments made to employee locations.
4. Missing / Unusual Customer Information - Analyze the Customer records to see if there
is missing information. Perhaps use the IF function to pinpoint cells with no data.
5. Uncollected A/R Accounts – Scrutinize all uncollected or severely delinquent A/R
balances, and investigate whether these are legitimate customers. If not, search audit
trail records to determine who set the customer up in the system. Use the SORT
commands or LARGE function to extract transactions with the highest number of days
outstanding.
6. Excessive Credit Memos - Similar to excessive voids, this technique can be used to cover
the theft of cash. A credit memo to a phony customer is written out, and the cash is taken
to make the total cash balance.
7. Write-off of Accounts Receivable - Comparing the write-off of receivables by customers
may lead to information indicating that the employee has absconded with customer
payments.
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8. Common Names and Addresses for Refunds - Sales employees frequently make bogus
refunds to customers for merchandise. The address shown for the refund is then made to
the employee's address, or to the address of a friend or co-worker.
9. Increasing Reconciling Items - Stolen deposits, or bogus checks written, are frequently
not removed, or covered, from the reconciliation. Hence, over a period of time, the
reconciling items tend to increase.
Looking for Fraud in the General Ledger
The general ledger is the backbone of the financial records, holding every business transaction.
Please note that sub-ledgers (i.e., accounts payable, fixed assets, inventory) may post in full detail
(i.e., every accounts payable invoice broken out to the actual detail) but normally this sub-ledger
activity is summarized on a periodic basis by account and posted. Regardless, the general ledger
is a treasure-trove for fraud reduction.
The easiest way to commit financial statement fraud is for a high-ranking officer to post a
nonstandard journal entry, falsifying the records. That way, the sub-ledger could show the
proper balance but such balance could be adjusted at the general ledger level, with the
nonstandard entry. Given this fact, a recent audit standard on fraud (SAS 99 – Consideration of
Fraud in a Financial Statement Audit) and later audit risk alerts point to the specific need to
review journal entries in the general ledger.
10. Stratify General Ledger Detail Information – Create a report listing the highest debit
records and credit records using the LARGE function.
11. Identify Nonstandard Journal Entries Made After Year End – Scrutinize all manual entry
records entered after year end and then summarize debits and credits by account.
12. General Ledger Out-of-Balance - When funds, merchandise, or assets are stolen and not
covered by a fictitious entry, the general ledger will be out of balance. An inventory of the
merchandise or cash is needed to confirm the existence of the missing assets.
13. Adjustments to Receivables or Payables - In cases where customer payments are
misappropriated, adjustments to receivables can be made to cover the shortage. Where
payables are adjusted, the perpetrator can use a phony billing scheme to convert cash to
his or her own use.
14. Looking for Fraud in the Sale of Assets - Some companies use the sale of appreciated
assets to hide losses from normal business operations and make the company appear
more profitable than it really is, therefore make sure that you investigate any significant
decreases in assets to determine if they were sold, then follow the receipts to make sure
they have been properly recorded.
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15. Capitalizing Costs - Capitalizing costs which have no future benefit is one way to enhance
current earnings. Make sure all assets are indeed assets with future value, else they need
to be written off.
16. Unusual Behavior - The perpetrator will often display unusual behavior, that when taken
as a whole is a strong indicator of fraud. The fraudster may not ever take a vacation or
call in sick in fear of being caught. He or she may not assign out work even when
overloaded. Other symptoms may be changes in behavior such as increased drinking,
smoking, defensiveness, and unusual irritability and suspiciousness.
17. Complaints - Frequently tips or complaints will be received which indicate that a
fraudulent action is going on. Complaints have been known to be some of the best sources
of fraud and should be taken seriously. Although all too often, the motives of the
complainant may be suspect, the allegations usually have merit that warrant further
investigation.
Looking for Fraud in Cash
18. Stale Items in Reconciliations - In bank reconciliations, deposits or checks not included in
the reconciliation could be indicative of theft. Missing deposits could mean the
perpetrator absconded with the funds; missing checks could indicate one made out to a
bogus payee.
19. Excessive Voids - Voided sales slips could mean that the sale was rung up, the payment
diverted to the use of the perpetrator, and the sales slip subsequently voided to cover the
theft.
20. Missing Documents - Documents which are unable to be located can be a red flag for
fraud. Although it is expected that some documents will be misplaced, the auditor should
look for explanations as to why the documents are missing, and what steps were taken to
locate the requested items. All too often, the auditors will select an alternate item or
allow the auditee to select an alternate without determining whether or not a problem
exists.
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Looking for Fraud in Payroll
21. Ghost Employees - Ghost employee schemes are frequently uncovered when an auditor,
fraud examiner, or other individual distributes paychecks to employees. Missing or
otherwise unaccounted for employees could indicate the existence of a ghost employee
scheme.
22. Employee Expense Accounts - Employees frequently conceal fraud in their individual
expense account reimbursements. These reimbursements should be scrutinized for
reasonableness and trends, especially in the area of cash transactions on the expense
account.
23. Large Payments to Individuals - Excessively large payments to individuals may indicate
instances of fraudulent disbursements.
24. Employee Overtime - Employees being paid for overtime hours not worked by altering
time sheets before or after management approval.
Looking for Fraud in Inventory
25. Inventory Shortages - Normal shrinkage over a period of time can be computed through
historical analysis. Excessive shrinkage could explain a host of fraudulent activity, from
embezzlement to theft of inventory.
26. Increased Scrap - In the manufacturing process, an increased amount of scrap could
indicate a scheme to steal and resell this material. Scrap is a favorite target of embezzlers
because it is usually subject to less scrutiny than regular inventory.
27. Post Office Boxes as Shipping Addresses - In instances where merchandise is shipped to
a post office box, this may indicate that an employee is shipping to a bogus purchaser.
28. Excess Purchases - Excess purchases can be used to cover fraud in two ways: 1. Fictitious
payees are used to convert funds; and 2. Excessive purchases may indicate a possible
payoff of purchasing agent.
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Data Analysis Tools
Preparing Data for Data Analysis
Before you start to analyze data using Excel’s various data commands such as Sort, Autofilter,
Subtotal, Grouping, Consolidate, or PivotTable, you should first inspect your data to determine
if it is in Analysis-Ready condition. In general, this means that the data must meet the following
criteria:
a. Contiguous Data – The data should contain no blank rows or blank columns. For example,
the screen below shows blank rows (with solid lines). These rows should first be removed
before proceeding with the creation of a PivotTable.
b. Single Row Data – Some accounting systems produce data that spans two or more rows
per transaction. If this is the case, your will need to clean that data so that all related
information for a single transaction or data is contained on a single row. For example, the
following data contains multiple rows of data related to a single sales order. In this case,
the user must move and paste the data to fall on a single row. This is an example of data
that requires a great deal of clean up.
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c. Column Headers - The data should contain a unique header atop each column. For
example, the following screen contains two columns labeled Date, while columns D and
E contain no heading. These are both cases of data that should be cleaned before creating
a PivotTable.
If you attempt to analyze data that does not contain a column heading atop all columns,
you will sometimes receive an error message, such as the example shown below.
If you have data with the same column heading used more than once, Excel will
sometimes alter the column headings, for example when you create a PivotTable, so all
headings will be unique.
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d. Row Descriptions – Generally, your data should repeat row descriptions for each row. For
example, the screen below shows that the state and city descriptions are not repeated
for each row in columns A & B.
A solutio for quickly filling in the missing row descriptions is presented later in these
materials.
e. Transposing Headers and Rows – In some cases, data may need to be transposed because
many of Excel’s Data tools use the column headings, not the row headings to crunch the
data. To do this, copy the data, then select Paste Special, Transpose, OK to flip the data
around.
f. Clean Data – The data must be clean of empty text cells containing spaces, special
characters, extra spaces within data, trailing spaces, trailing zeros, leading zeros, etc.
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Data Analysis Tools
Excel provides specialized tools for analyzing data and generating financial reports, yet most CPAs
are unaware of these tools or haven’t tried using them before. Specifically useful are the
Subtotaling, Grand Totaling, Filtering, Consolidating, Grouping & Outlining, Drilling, OLAP Data
Cubes, PivotTables, Sparklines, Data Bar Reporting, Conditional Formatting, Charting, Foot
Notes and End Notes, Formula Auditing Tools, Error Checking, Functions, and Data Analysis
Tools.
The concepts discussed are intended to directly aide the CPA in summarizing, slicing, dicing and
analyzing data, and generating related financial reports.
2013 Data Ribbon:
2013 Insert Ribbon:
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Data Sort
You would think that every Excel user would already know all about sorting data in Excel, but I am
frequently surprised to find that many users have missed a few key points related to using this
tool. I don’t mean to belittle you are talk beneath you, but humor me a copy of paragraphs and
let’s make sure you are fully up to speed on the following key sorting points:
1. Contiguous Data - The “A to Z” sorting tool can sort a large matrix of data without having to
highlight the area as long as the data is contiguous; that is to say that your data should contain
no blank columns, no blank rows, and the columns must all be labeled with a column heading.
When data is contiguous, all you need to do is place your cursor in a single cell in a given
column and click the Sort A to Z or Sort Z to A buttons, and Excel will automatically select the
entire matrix for sorting. Surprisingly many users waste a great deal of time highlighting sort
ranges prior to sorting, but this step is often unnecessary.
2. A to Z Button - Simply place the cursor in the desired column for sorting, and press the A to Z
or Z to A button as the case may be. Excel will automatically sort all continuous columns that
have headings and all contiguous rows from the top row under the heading labels down to the
last row in the selected column that contains data. (Note - If you accidently select 2 cells
instead of just one, your results will not be correct.)
3. Sort by 64 Columns - The “Sort” tool was enhanced beginning in Excel 2007 as it now provides
the ability to sort by up to 64 columns, instead of just 3 columns. Presented below is a dialog
box which shows this expanded functionality.
4. Sort Left to Right – Excel has always provided the ability to sort left to right. To do so, select
the Sort Options box in the Sort dialog box and click the check box labeled Sort Left to Right
as pictured below.
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5. Sort by Color – beginning with Excel 2007, you can also sort by font color or by cell color, or
both. This is handy in many ways. Sometimes CPAs use color to tag or mark certain cells - and
later find it useful to be able to sort by those markings. In other situations CPAs use conditional
formatting to apply color to cells using a wide variety of rules; and thereafter they can sort the
data based on the resulting conditional colors. The two sort-by-color options are pictured
below.
To be fair, it was sort of possible to sort by color in Excel 2003. To accomplish this task, you
needed to use the CELL function in order to identify information about a given cell such as the
cell color or font color. Thereafter, the results of that function could be used to sort rows –
which effectively means that you can sort by color in Excel 2003 – but it takes a bit more effort.
6. Sort By Custom List – Another sorting capability in Excel is the ability to sort by Custom List.
For example, assume a CPA firm has ten partners, and the Managing partner prefers to be
shown at the top of the list, and the remaining Partners based on seniority. In this case, you
could create a Custom List in the Excel Options dialog box listing the partners in the desired
order, and then sort future reports based on that order.
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To access the Custom List settings:
1. In Excel 2013 & 2010, select File, Options, Advanced, and scroll to the bottom, and then
select Edit Custom List.
2. In Excel 2007, select File, Options, select Edit Custom List option a few inches down from
the top.
3. In Excel 2003, select Tools, Options, and click the Custom Lists tab.
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Filtering Data
AutoFilter allows you to view a subset of your data and when you are done, you can clear the
filters to once again redisplay all of your data. To use this tool, start with any list of data and turn
on the AutoFilter tool. Then position your cursor in the column you want to filter and use the
drop down arrows to apply your filters as suggested in the screen below.
Once the filters are applied, you will see a subset of your data. For example, the screen presented
below shows filtered data for only Macon and Savannah properties.
As filters are applied, a small funnel icon appears in the drop down arrow button to indicate that
a filter has been applied to that particular column.
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Key Points Concerning the AutoFilter Command:
1. Contiguous Data – The AutoFilter tools works best when you are working with data that is
contiguous. In other words, your data should contain no blank columns, no blank rows,
and the columns must all be labeled.
2. Column Headings –Your columns need unique column headings in and single row, and if
the column headings are not in row 1, then the row above the column headings should be
blank so Excel will auto detect the correct range.
3. Filter by Multiple Columns - You can filter by more than one column.
4. Filters are Additive - Each additional filter is based on the current filter and further reduces
the subset of data.
5. Removing Filters – In all editions of Excel, a fast way to remove multiple filters is to turn
AutoFilter off and then turn AutoFilter back on. In Excel 2007 and later editions, you can
also click the Clear button in the Sort & Filter Group as pictured below.
6. Filter by Color – You can filter based on colors. For example, you can filter by cell color or
by a list of numbers, you can filter by icon or by a custom filter.
Note that the Color Filter is mutually exclusive as you cannot also filter by value or text
when filter by color is applied, and vice versa.
7. Filters Enabled - A drop-down arrow
8. Filter Applied - A Filter button
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means that filtering is enabled but not applied.
means that a filter is applied.
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9. Filter Spanning - The commands
under the All Dates in the Date
Filters menu, such as January or
Quarter 2 filter by the period no
matter what the year. This can be
useful, for example, to compare
sales by a period across several
years.
10. This Year vs. Year-to-Date - This
Year and Year-to-Date are
different in the way that future
dates are handled. This Year
filtering can return dates in the
future for the current year,
whereas Year-to-Date only
returns dates up to and including
the current date based on the
computer’s time clock.
11. Filtering Dates - All date filters are based on the Gregorian calendar as decreed by Pope
Gregory XIII, after whom the calendar was named, on 24 February 1582. The Gregorian
calendar modifies the Julian calendar's regular four-year cycle of leap years as follows:
Every year that is exactly divisible by four is a leap year, except for years that are exactly
divisible by 100; the centurial years that are exactly divisible by 400 are still leap years. For
example, the year 1900 is not a leap year; the year 2000 is a leap year.
12. Filtering By Days of Week - If you want to filter by days of the week, simply format the
cells to show the day of the week, or
insert a new column and use the
WEEKDAY function to calculate the
week day, and then apply filters using
this new column.
13. Top & Bottom Filtering - On the Data
tab, in the Sort & Filter group, click
Filter. Point to Number Filters and
then select Top 10. To filter by
number, click Items. To filter by
percentage, click Percent. Note - Top
and bottom values are based on the
original range of cells or table column
and not the filtered subset of data.
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14. Above & Below Average Filtering - On the Data tab, in the Sort & Filter group, click Filter.
Select Number Filters, Above/Below Average. Note – These values are based on the
original range of cells or table column and not the filtered subset of data.
15. Filtering Out Blanks - To filter out blanks, in the AutoFilter menu at the bottom of the list
of values, de-select the check box labeled Blanks.
16. Filtering By Color - Select Filter by Color, and then depending on the type of format, select
Filter by Cell Color, Filter by Font Color, or Filter by Cell Icon. Note that these filter options
only show up when there are actual cell colors, font colors or icons included in the data
range.
17. Filter by Selection - To filter by text, number, date, time, or color for selected cell(s), select
the cells to be used as a filter basis and then right-click that selection, and from the popup
menu select Filter, Filter by Selected Cell's Value, (or Filter by Selected Cell's Color, Filter
by Selected Cell's Font Color, or Filter by Selected Cell's Icon).
18. Refreshing Filters - To reapply a filter after the data changes, click a cell in the range or
table, and then on the Data tab, in the Sort & Filter group, click Reapply.
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Data Form
Excel’s Data Form tool provides a data input window which makes Excel look and behave more
like a database, such as Microsoft Access. (Note that in Excel 2013, 2010 and 2007, the Form tool
button has not been included on the Ribbon, so to use it you will first need to add the Form tool
button to the Quick Access Toolbar.)
A data form provides a convenient means to enter or display one complete row of information
in a range or table without scrolling horizontally. Some people, especially those who are used to
using databases, find that using a data form can make data entry easier than moving from column
to column when you have more columns of data than can be viewed on the screen.
Key Points using Data Form:
1. You cannot print data from a data form.
2. Because a data form is a modal dialog box, you cannot use either the Excel Print command
or Print button until you close the data form.
3. You might consider using the Windows Print Screen key to make an image of the form,
and then paste it into Microsoft Word for printing.
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Data Subtotals
Excel’s Subtotal command automatically calculates and inserts subtotals and grand totals in your
list or table. Once inserted, Excel recalculates subtotal and grand totals as you enter and edit the
detail data. The Subtotal command also outlines the list so that you can display and hide the
detail rows for each subtotal. Examples of the Subtotal dialog box and a resulting subtotaled
table are shown below.
To display subtotals and grand totals at the top instead of the bottom, deselect the checkbox
labeled Summary below data.
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Key points to Consider When Using Subtotaling are as follows:
1. Contiguous Data – The Subtotal tools works best when you are working with data that is
contiguous. In other words, your data should contain no blank columns, no blank rows,
and the columns must all be labeled.
2. Sort Before You Subtotal - You must sort the data by the column you wish to subtotal by,
else you will receive erroneous results.
3. Other Mathematical Applications - The Subtotal tool not only calculates subtotals, but it
can also calculate minimums, maximums, averages, standard deviations, and other
functions.
4. Subtotals in 2013, 2010 & 2007 Tables – Excel 2007 added a new Table tool which enables
Subtotals a little differently; the Subtotal tool appears at the bottom of each column in
each Table, as shown in the screen below.
5. Automatic Outlining – The Subtotal tool automatically inserts Outlines, which allows you
to collapse or expand your data.
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6. Copying Outline Data - Some CPAs also like to copy and paste collapsed subtotal data to
another location, but they find this process copies and pastes all of the data – not just the
summary data they desire. In this situation, there are two ways to achieve a clean copy
and paste without grabbing all the hidden data as follows:
a. CTRL key – Hold the Control Key down while you individually click to select
individual rows; this action will enable you to copy and paste selected data.
However, this approach can sometimes be problematic because if you miss-click,
you have to start over.
b. Select Visible Cells – A better approach is to use the Select Visible Cells tool. This
tool will select on the data you can see, after which the copy and paste routine
will yield the desired results. This option is better because it is faster and less error
prone.
c. Go To – You can also select visible cells using Go To. To do this, press F5 to launch
the Go To tool and then click Special. In the Go To Special dialog box, select the
radio button labeled Select Visible cells and press OK.
d. ALT + ; - The Alt + ; key combination is the shortcut to using the Select Visible Cells
Tool.
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Data Validation
Data Validation can be used to limit the data that can be entered into a cell. For example, you
might want the user to enter only values between 1% and 99%. You might also use this tool to
enable data input to a drop down list which offers two advantages in that it can be faster and
more accurate. To create a dropdown list, enter a list into sequentially cells in Excel. Next, from
the Data tab select Data Validation, Data Validation (yes, again), then in the dialog box (as shown
below) select List from the Allow dropdown box and then indicate the data range for your list in
the Source box.
After making all the necessary selections in the validation list dialog box, your worksheet will
produce a cell containing a drop down list (shown in cell A10 below) that behaves as shown.
You can also provide messages to define what input you expect for the cell, and instructions to
help users correct any errors. For example, on a worksheet, you can set up a cell to allow only
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account numbers that are exactly three characters long. When users select the cell, you can show
them a message such as this one:
If users ignore this message and type invalid data in the cell, such as a two-digit or five-digit
number, you can display an actual error message. In a more advanced scenario, you might use
data validation to calculate the maximum allowed value in a cell based on a value elsewhere in
the workbook. In the following example, the user has typed $4,000 in cell E7, which exceeds the
maximum limit specified for commissions and bonuses.
If the payroll budget were to increase or decrease, the allowed maximum in E7 would
automatically increase or decrease with it.
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Data Table (“What-if Analysis”)
Data tables are part of the collection of what-if analysis commands, which include:
1. Data Tables
2. Goal Seek
3. Scenarios
The Data Table command enables the process of changing values in cells to see how those changes
will affect the outcome. For example, you can use a data table to vary the interest rate and term
length used in a loan to determine possible monthly payment amounts.
There are two types of Data Tables – One Way and Two Way. A data table cannot accommodate
more than two variables. If you want to analyze more than two variables, you should use
scenarios. Although it is limited to only one or two criterion (one for the row input cell and one
for the column input cell), each criterion can include as many different variable values as you want.
(In contrast, a Scenario can have a maximum of 32 different criterion, but you can create as many
Scenarios as you want.)
Loan Analysis Example
In this exercise, we start by creating a simple Payment function to calculate the payment amount
of a loan given a loan amount, interest rate and number of periods.
The next step is to create a Two-Way Data Table displaying the resulting payment amount given
a variety of lengths of the loan. This process is started by creating a list of the alternative loan
amounts, as shown below in B8, B9, B10, etc. Cell C7 must reference the results you want to be
displayed in the table.
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Next, highlight the data table range and use the Data Table command on the Data tab (as
shown below) to generate the desired table.
This process will generate the following table:
This table tells us that the same loan amount will require a monthly payment of $4,972 to pay
the loan off in just 6 years, or a monthly payment of $5,800 to repay the loan in just 5 years.
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The next step in this exercise is to generate a line chart based on the data table we just created.
This line chart will provide some interesting observations regarding the benefits and detriments
of paying off loans over longer periods.
The resulting chart is shown as follows:
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Based on this, no one should ever obtain a fair market loan for more than 15 years, the reduction
in payments simply aren’t worth the additional length of the loan. This same basic behavior is
seen whether the interest rate is 1% or 100%, or whether the loan amount is $1,000 or
$10,000,000. The only time you might be justified in obtaining a loan longer than 15 years might
be when you are extended a favorable interest rate (perhaps from a rich uncle), better than a fair
market interest rate.
Goal Seek
If you know the result that you want from a formula, but are not sure what input values are
needed to produce your desired results, use Goal Seek. For example, suppose that you have
decided to purchase a house, but you don’t know how much house you can afford. In this case,
know how the interest rate (3.75%) and how long you want to take to pay off the loan (15 years),
and the amount you can afford to pay each month ($2,800). In this case, you can use Goal Seek
to work backwards to figure out how much house you can afford. Start by calculating the monthly
payment based on any random home loan amount as pictured below.
Next, from the Data tab, select What-If Analysis, Goal Seek. Fill in the parameters to set the
payment amount to $2800 by adjusting the Loan Amount, as shown, and then click OK.
The result is that a person with $2,800 available to make monthly payments can afford to
purchase a home costing up to $385,027 (assuming a 15 year loan and 3.75% interest rate) – as
pictured above. (Keep in mind that anyone actually following this scenario would need to
consider that homes also come with other monthly obligations including real estate taxes,
insurance maintenance, etc.)
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Data - Text to Columns
CPAs sometimes receive data from their clients or IT departments that is in text form. When this
happens, Excel can split the contents of one or more cells in a column and distribute those
contents as individual parts across other cells in adjacent columns. For example, the worksheet
below contains a column of full names and amounts that you want to split into separate columns.
The Text to Columns wizard parses the data automatically into separate cells. To use this tool,
select the cell, range or entire column that contains the text values that you want to split.
Notes:
1. A range that you want to split can include any number of rows, but it can include no more than
one column.
2. You also should make sure there are enough blank columns to the right of the selected column to
prevent overwriting existing data in those adjacent columns.
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Flash Fill
Of all the Office 2013 applications, Excel is the beneficiary of the most impressive enhancements.
Excel’s new Flash Fill watches you work and applies logic to help you complete your tasks. The
example pictured below contains a list of 44 first and last names in Column A, which I want to
separate into Columns B and C. As I start typing the first name of the second record in Column B;
Excel’s Flash Fill guesses what I’m trying to do and offers to fill in the remaining 42 first names
(as shown in grey text).
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Data Grouping & Outlining
If you have a list of data that you want to group and summarize, you can create an outline of up
to eight levels. Each inner level (represented by a higher number in the outline symbols) displays
detailed data for the preceding outer level, represented by a lower number in the outline
symbols. Use an outline to quickly display summary rows or columns, or to reveal the detail data
for each group. You can create an outline of rows (as shown in the example below), an outline of
columns, or an outline of both rows and columns.
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PowerView
Excel’s new PowerView inserts new worksheets connected to your data, and then enables you
to create new report types, such as the interactive map chart presented below. The resulting
PowerView Map report is zoomable, and filters can be applied to display partial data.
PowerView worksheets can be published as standalone, interactive reports to Microsoft
SharePoint’s PowerPivot Gallery or other reporting service destinations. Some of the tools
provided by PowerView include the ability to create a dashboard containing multiple
PowerViews, apply themes and backgrounds, insert pictures and text boxes, insert collapsible
and expandable tiles, and add data slicers.
PowerView Learning Points
1. Included - PowerView isn’t included in Office Home editions. Power View and PowerPivot are only
available in the Office Professional Plus and Office 365 Professional Plus editions.
2. Worksheet - PowerView is another sheet in the workbook, and acts like a Dashboard.
3. Fields – Add data to the PowerView by selecting fields, much like you do for PivotTables.
4. Play - You can play charts to see how they change over time.
5. PowerView uses PowerPivot - Known to be extremely fast for retrieving and sorting data.
6. Relationships – PowerView can integrate multiple data sets via relationships.
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Timeline Slicer
CPAs who work with PivotTables will likely appreciate Excel’s new Timeline Slicer which helps
users slice and dice Pivot data that contain dates. As an example, selecting the dates May through
October on the Timeline slicer pictured below adjusts the PivotTable to display May thru October
data.
Quick Analysis
Excel’s Quick Analysis tool also helps you analyze data by offering a variety of formatting, charts,
totals, tables and sparkline layouts to instantly summarize large volumes of data (see screen
below). When using Quick Analysis to scrutinize text-only data, text specific options for
highlighting duplicate or unique text items appear.
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Queries
Excel 2010, 2007 & 2003 include pre-designed “queries” that can import commonly used data
such as stock quotes for updating a stock portfolio. All you need is a connection to the internet
and of course, some stock ticker symbols. In Excel 2010 or 2007 select Data, Existing
Connections, MSN MoneyCentral Stock Quotes (or in Excel 2003 select Data, Import External
Data, Import Data Existing Connections, MSN MoneyCentral Stock Quotes) and then walk
through the web query wizard for importing stock quotes. In just a few seconds, Excel will retrieve
Real-Time data for NYSE, NASDAQ & AMEX, and 20 minute delayed stock prices from other
exchanges (during the hours when the stock market is open) and display a grid of complete upto-date stock price information that is synchronized to the stock market’s changing stock prices.
With each click of the “Refresh” button, the stock price information in Excel is updated - this sure
beats picking numbers out of the newspaper.
Completing the Stock Portfolio – Next link the grid data to another worksheet, and insert new
columns containing the number of shares owned, as wells as an additional column to compute
the total value based on shares owned, as shown below.
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Refreshing the Stock Prices - Once you have created your portfolio, simply click the Refresh Data
button on the “External Data” Toolbar in Excel 2003 or on the “Data Ribbon” in Excel 2010 &
2007 shown below to update the current value of your Portfolio.
Query Parameters - There are numerous options to help you extract exactly the data you want
the way you want it. The “Web Query Parameters Box”, “Web Query Options box” and “External
Data Properties Box” provide numerous options for controlling your web query.
Excel 2013 Stock Quote Queries
In Excel 2013, for unknown reasons Microsoft has removed the stock quote query option,
therefore below are instructions for restoring this option.
1. Launch Notepad (Start, Programs, Windows Accessories, Notepad)
2. Enter the following information exactly:
Web
1
http://moneycentral.msn.com/investor/external/excel/quotes.asp?SYMBOL=["QUOTE","Ent
er stock, fund or other MSN MoneyCentral Investor symbols separated by commas."]
Or if you prefer, use this to query Yahoo’s stock prices:
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3. Save the file using any name you want, but be sure to include the extension .igy as
pictured.
4. Make sure to save this file to the folder labeled My Data Sources.
5. Now in Excel, from the Data tab select Existing Queries, then scroll to and launch the new
query you just created – it should work just like it did in Excel 2010, 2007 and 2003.
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Database Queries
Microsoft Excel can also query and retrieve data you want from an external data source. For
example, you can retrieve Microsoft Excel data about a specific product by region. You can create
a simple query by using the Query Wizard, or you can create a more complex query by using the
advanced features of Microsoft Query.
To use Microsoft Query to retrieve external data, you must:
1. Have access to an external data source - If the data is not on your local computer, you
may need to see the administrator of the external database for a password, user
permission, or other information about how to connect to the database.
2. Install Microsoft Query - If Microsoft Query is not available, you might need to install it.
3. Specify a source to retrieve data from, and then start using Microsoft Query - For
example, if you want to insert database information, display the Database toolbar, click
Insert Database, click Get Data, and then click MS Query.
For example, suppose we have some data in our accounting system – Sage MAS 200 ERP that we
would like to analyze in Excel. We can use the Database Query Wizard to build a query that will
extract the data we need and place it in an Excel spreadsheet, as follows.
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The first step is to select the type of database you want to query and to select the specific
database.
Upon the selection of the desired database a list of tables will be presented. Choose the desired
tables, and select the desired data fields to be imported. You will then have the option to filter
and sort the data before it is imported. Finally you will be given the option to save the query so
you can run it at a later date without having to start from scratch. Excel will then return a table
full of the data you requested as shown in the screen below.
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Sparklines
Sparklines – Sparklines are small cell-sized charts that you can embed in a worksheet next to
data to get a quick visual representation of the data. For example, if you had a worksheet that
tracked the performance of several dozen stocks, you could create a Sparkline for each stock
that graphed its performance over time, in a very compact way. Here are examples:
Conditional Formatting
1. Conditional Formatting Improvements – Microsoft has improved and added more styles
and icons regarding the ability to apply a format to a range of cells, and then have the
formatting change according to the value of the cell or formula.
2. Solid Bars - Data Bars can be solid now in Excel 2010. Excel 2007 bars offered only a
gradient effect, which was visually was confusing to read. Below is a comparison on the
Excel 2007 and Excel 2010 Data Bar options.
2007 Gradient
2010 Gradient
2010 Solid
3. Negative Numbers - Microsoft corrected a problem which Excel 2007 had when creating
Data Bars based on negative numbers, by adding axis support for both positive and
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negative values. The screens shots below show Excel 2010’s new solution, and how Excel 2007
got confused when applying Data Bars to the exact same data.
Excel 2010’s Data Bars
Excel 2007’s Data Bars
a. More Data Bar Options - Notice that Microsoft added more Data Bar options as shown
in the comparison below.
Excel 2010’s Data Bar Menu
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b. More Icon Options - Notice that Microsoft added more Data Bar options as shown in
the comparison below.
Excel 2010’s Icon Menu
Excel 2007’s Icon Menu
c. Arrow Colors - It is possible to change icons used for KPI i.e. You can have two arrows
with different colors that might represent both a negative impact and positive growth,
for example.
d. Referring to Data - You can now refer to data on different worksheets, and even refer to
a range outside the Conditional Formatting area. The screen below shows the error
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message produced by Excel 2007 when you attempted to reference cells outside the
Conditional Formatting area; Excel 2010 now accommodates this situation.
Excel 2007
Error
Message
4. Sort by Color – Beginning with 2007, Excel provides the ability to sort by font color or by
cell color, or both. This is handy in many ways. Sometimes CPAs use color to tag or mark
certain cells - and later find it useful to be able to sort by those markings. In other situations
CPAs use conditional formatting to apply color to cells using a wide variety of rules.
Thereafter Excel can sort the data based on the resulting colors. The sort-by-color options
are shown below.
To be accurate, it was possible to sort by color in Excel 2003. To accomplish this task, you
needed to use the =CELL function in order to identify information about a given cell such as
the cell color or font color. Thereafter, the results of that function could be used to sort rows
– which effectively means that you can sort by color in Excel 2003 – but it takes a bit more
effort.
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Excel Functions
An Excel function is a preset formula that calculates a specific result based on the
criteria/variables/arguments you specify. All functions start with the equal sign followed by the
function’s name and criteria/variables/arguments you specify. As a simple example, the most
frequently used function in Excel is the SUM function, which is used to add data.
There are a total of 455 Excel Functions in Excel 2013; the following table summarizes the number
of functions introduced in previous editions of Excel.
Excel Functions are preprogrammed formulas that make the task of writing complex formulas
easier. There are a total of 455 functions in Excel. These functions are separated into 14
categories as follows:
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Categories & Number Of Functions In Each Category
1. Compatibility .................................................................. (38)
2. Cubes ................................................................................ (7)
3. Databases ....................................................................... (12)
4. Date and times ............................................................... (24)
5. Engineering .................................................................... (54)
6. Financials ........................................................................ (55)
7. Information .................................................................... (20)
8. Logical .............................................................................. (9)
9. Lookup and references .................................................. (19)
10. Math and trigonometry ................................................. (79)
11. Statistical ...................................................................... (101)
12. Texts ............................................................................... (30)
13. User defined that are installed with add-ins ................... (4)
14. Webs ................................................................................ (3)
Function Relevance to CPAs
Some Excel functions are more powerful than others and some are more relevant to the CPA
than others. For example, most CPAs will find the IF, SUM, COUNT, SUBTOTAL, TEXT, and
VLOOKUP are very relevant to the CPA while other engineering and trigonometry functions such
as LOG, PI, RADIENS, DELTA, TAN, COMPLEX, and HAX2DEC are typically less relevant to CPAs. It
has been my experience that approximately 171 functions are more relevant or important to
CPAs; therefore in my opinion, CPAs wishing to increase their command of Excel functions should
concentrate on learning these functions primarily. To help you accomplish this goal, presented
below is a list of all 455 Excel functions, along with a brief explanation of each function. The 170
functions that I find more relevant are shown in red bold.
Compatibility
1
2
BETADIST
BETAINV
3
4
5
BINOMDIST
CHIDIST
CHIINV
6
7
8
CHITEST
CONFIDENCE
COVAR
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Returns the beta cumulative distribution
Returns the inverse of the cumulative distribution for a specified
beta distribution
Returns the individual term binomial distribution probability
Returns the one-tailed probability of the chi-squared distribution
Returns the inverse of the one-tailed probability of the chi-squared
distribution
Returns the test for independence
Returns the confidence interval for a population mean
Returns covariance, the avg of the products of paired deviations
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9
CRITBINOM
Returns the smallest value for which the cumulative binomial
distribution is less than or equal to a criterion value
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
EXPONDIST
FDIST
FINV
FTEST
GAMMADIST
GAMMAINV
HYPGEOMDIST
LOGINV
LOGNORMDIST
MODE
NEGBINOMDIST
NORMDIST
NORMINV
NORMSDIST
NORMSINV
PERCENTILE
PERCENTRANK
POISSON
QUARTILE
RANK
STDEV
STDEVP
TDIST
TINV
TTEST
VAR
VARP
WEIBULL
ZTEST
Returns the exponential distribution
Returns the F probability distribution
Returns the inverse of the F probability distribution
Returns the result of an F-test
Returns the gamma distribution
Returns the inverse of the gamma cumulative distribution
Returns the hypergeometric distribution
Returns the inverse of the lognormal cumulative distribution
Returns the cumulative lognormal distribution
Returns the most common value in a data set
Returns the negative binomial distribution
Returns the normal cumulative distribution
Returns the inverse of the normal cumulative distribution
Returns the standard normal cumulative distribution
Returns the inverse of the standard normal cumulative distribution
Returns the k-th percentile of values in a range
Returns the percentage rank of a value in a data set
Returns the Poisson distribution
Returns the quartile of a data set
Returns the rank of a number in a list of numbers
Estimates standard deviation based on a sample
Calculates standard deviation based on the entire population
Returns the Student's t-distribution
Returns the inverse of the Student's t-distribution
Returns the probability associated with a Student's t-test
Estimates variance based on a sample
Calculates variance based on the entire population
Returns the Weibull distribution
Returns the one-tailed probability-value of a z-test
39
CUBEKPIMEMBER
40
CUBEMEMBER
Returns a key performance indicator (KPI) property and displays
the KPI name in the cell. A KPI is a quantifiable measurement,
such as monthly gross profit or quarterly employee turnover, that
is used to monitor an organization's performance.
Returns a member or tuple from the cube. Use to validate that the
member or tuple exists in the cube.
Cubes
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41
CUBEMEMBERPROPER
TY
42
CUBERANKEDMEMBER
43
CUBESET
44
45
CUBESETCOUNT
CUBEVALUE
Returns the value of a member property from the cube. Use to
validate that a member name exists within the cube and to return
the specified property for this member.
Returns the nth, or ranked, member in a set. Use to return one or
more elements in a set, such as the top 10 sales performers.
Defines a calculated set of members or tuples by sending a set
expression to the cube on the server, which creates the set, and
then returns that set to Microsoft Office Excel.
Returns the number of items in a set.
Returns an aggregated value from the cube.
Databases
46
47
48
49
DAVERAGE
DCOUNT
DCOUNTA
DGET
50
51
52
DMAX
DMIN
DPRODUCT
53
54
DSTDEV
DSTDEVP
55
DSUM
56
57
DVAR
DVARP
Returns the average of selected database entries
Counts the cells that contain numbers in a database
Counts nonblank cells in a database
Extracts from a database a single record that matches the
specified criteria
Returns the maximum value from selected database entries
Returns the minimum value from selected database entries
Multiplies the values in a particular field of records that match the
criteria in a database
Estimates standard deviation based on a sample of the database
Calculates the standard deviation based on the entire population
of selected database entries
Adds the numbers in the field column of records in the database
that match the criteria
Estimates variance based on a sample of the database
Calculates variance based on the entire population of selected
database entries
Date and Time
58
59
60
61
62
DATE
DATEVALUE
DAY
DAYS
DAYS360
63
EDATE
64
EOMONTH
65
HOUR
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Returns the serial number of a particular date
Converts a date in the form of text to a serial number
Converts a serial number to a day of the month
Returns the number of days between two dates
Calculates the number of days between two dates based on a 360day year
Returns the serial number of the date that is the indicated number
of months before or after the start date
Returns the serial number of the last day of the month before or
after a specified number of months
Converts a serial number to an hour
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66
ISOWEEKNUM
67
68
69
70
MINUTE
MONTH
NETWORKDAYS
NETWORKDAYS.INTL
71
72
73
74
75
76
77
NOW
SECOND
TIME
TIMEVALUE
TODAY
WEEKDAY
WEEKNUM
78
WORKDAY
79
WORKDAY.INTL
80
81
YEAR
YEARFRAC
Returns the number of the ISO week number of the year for a
given date
Converts a serial number to a minute
Converts a serial number to a month
Returns the number of whole workdays between two dates
Returns the number of whole workdays between two dates using
parameters to indicate how many days are weekend days
Returns the serial number of the current date and time
Converts a serial number to a second
Returns the serial number of a particular time
Converts a time in the form of text to a serial number
Returns the serial number of today's date
Converts a serial number to a day of the week
Converts a serial number to a number representing where the
week falls numerically with a year
Returns the serial number of the date before or after a specified
number of workdays
Returns the serial number of the date before or after a specified
number of workdays using parameters to indicate which and how
many days are weekend days
Converts a serial number to a year
Returns the year fraction representing the number of whole days
between start_date and end_date
Engineering
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
BESSELI
BESSELJ
BESSELK
BESSELY
BIN2DEC
BIN2HEX
BIN2OCT
BITAND
BITLSHIFT
BITOR
BITRSHIFT
BITXOR
COMPLEX
CONVERT
DEC2BIN
DEC2HEX
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Returns the modified Bessel In(x)
Returns the Bessel Jn(x)
Returns the modified Bessel Kn(x)
Returns the Bessel Yn(x)
Converts a binary number to decimal
Converts a binary number to hexadecimal
Converts a binary number to octal
Returns a 'Bitwise And' of two numbers
Returns a value number shifted left by shift_amount bits
Returns a bitwise OR of 2 numbers
Returns a value number shifted right by shift_amount bits
Returns a bitwise 'Exclusive Or' of two numbers
Converts real and imaginary coefficients into a complex number
Converts a number from one measurement system to another
Converts a decimal number to binary
Converts a decimal number to hexadecimal
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98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
DEC2OCT
DELTA
ERF
ERF.PRECISE
ERFC
ERFC.PRECISE
GESTEP
HEX2BIN
HEX2DEC
HEX2OCT
IMABS
IMAGINARY
IMARGUMENT
IMCONJUGATE
IMCOS
IMCOSH
IMCOT
IMCSC
IMCSCH
IMDIV
IMEXP
IMLN
IMLOG10
IMLOG2
IMPOWER
IMPRODUCT
IMREAL
IMSEC
IMSECH
IMSIN
IMSINH
IMSQRT
IMSUB
IMSUM
IMTAN
OCT2BIN
OCT2DEC
OCT2HEX
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Converts a decimal number to octal
Tests whether two values are equal
Returns the error
Returns the error
Returns the complementary error
Returns the complementary ERF integrated between x and infinity
Tests whether a number is greater than a threshold value
Converts a hexadecimal number to binary
Converts a hexadecimal number to decimal
Converts a hexadecimal number to octal
Returns the absolute value (modulus) of a complex number
Returns the imaginary coefficient of a complex number
Returns the argument theta, an angle expressed in radians
Returns the complex conjugate of a complex number
Returns the cosine of a complex number
Returns the hyperbolic cosine of a complex number
Returns the cotangent of a complex number
Returns the cosecant of a complex number
Returns the hyperbolic cosecant of a complex number
Returns the quotient of two complex numbers
Returns the exponential of a complex number
Returns the natural logarithm of a complex number
Returns the base-10 logarithm of a complex number
Returns the base-2 logarithm of a complex number
Returns a complex number raised to an integer power
Returns the product of from 2 to 255 complex numbers
Returns the real coefficient of a complex number
Returns the secant of a complex number
Returns the hyperbolic secant of a complex number
Returns the sine of a complex number
Returns the hyperbolic sine of a complex number
Returns the square root of a complex number
Returns the difference between two complex numbers
Returns the sum of complex numbers
Returns the tangent of a complex number
Converts an octal number to binary
Converts an octal number to decimal
Converts an octal number to hexadecimal
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Financial
136
137
138
ACCRINT
ACCRINTM
AMORDEGRC
139
140
AMORLINC
COUPDAYBS
141
COUPDAYS
142
COUPDAYSNC
143
144
COUPNCD
COUPNUM
145
146
147
148
COUPPCD
CUMIPMT
CUMPRINC
DB
149
DDB
150
151
DISC
DOLLARDE
152
DOLLARFR
153
DURATION
154
155
156
EFFECT
FV
FVSCHEDULE
157
158
159
160
INTRATE
IPMT
IRR
ISPMT
161
MDURATION
162
MIRR
163
NOMINAL
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Returns accrued interest for a security paying periodic interest
Returns accrued interest for a security paying interest at maturity
Returns the depreciation for each accounting period by using a
depreciation coefficient
Returns the depreciation for each accounting period
Returns the number of days from the beginning of the coupon
period to the settlement date
Returns the number of days in the coupon period that contains the
settlement date
Returns the number of days from the settlement date to the next
coupon date
Returns the next coupon date after the settlement date
Returns the number of coupons payable between the settlement
date and maturity date
Returns the previous coupon date before the settlement date
Returns the cumulative interest paid between two periods
Returns cumulative principal paid on a loan between two periods
Returns the depreciation of an asset for a specified period by using
the fixed-declining balance method
Returns the depreciation of an asset for a specified period by
using the double-declining balance method or some other
method that you specify
Returns the discount rate for a security
Converts a dollar price, expressed as a fraction, into a dollar price,
expressed as a decimal number
Converts a dollar price, expressed as a decimal number, into a
dollar price, expressed as a fraction
Returns the annual duration of a security with periodic interest
payments
Returns the effective annual interest rate
Returns the future value of an investment
Returns the future value of an initial principal after applying a
series of compound interest rates
Returns the interest rate for a fully invested security
Returns interest payment for an investment for a given period
Returns the internal rate of return for a series of cash flows
Calculates the interest paid during a specific period of an
investment
Returns the Macauley modified duration for a security with an
assumed par value of $100
Returns the internal rate of return where positive and negative
cash flows are financed at different rates
Returns the annual nominal interest rate
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164
165
NPER
NPV
Returns the number of periods for an investment
Returns the net present value of an investment based on a series
of periodic cash flows and a discount rate
166
ODDFPRICE
167
168
ODDFYIELD
ODDLPRICE
169
170
ODDLYIELD
PDURATION
171
172
PMT
PPMT
173
PRICE
174
175
PRICEDISC
PRICEMAT
176
177
178
PV
RATE
RECEIVED
179
RRI
180
181
SLN
SYD
182
183
184
185
TBILLEQ
TBILLPRICE
TBILLYIELD
VDB
Returns price per $100 face value of a security with an odd first
period
Returns the yield of a security with an odd first period
Returns the price per $100 face value of a security with an odd last
period
Returns the yield of a security with an odd last period
Returns the number of periods required by an investment to reach
a specified value
Returns the periodic payment for an annuity
Returns the payment on the principal for an investment for a
given period
Returns the price per $100 face value of a security that pays
periodic interest
Returns the price per $100 face value of a discounted security
Returns the price per $100 face value of a security that pays
interest at maturity
Returns the present value of an investment
Returns the interest rate per period of an annuity
Returns the amount received at maturity for a fully invested
security
Returns an equivalent interest rate for the growth of an
investment
Returns the straight-line depreciation of an asset for one period
Returns the sum-of-years' digits depreciation of an asset for a
specified period
Returns the bond-equivalent yield for a Treasury bill
Returns the price per $100 face value for a Treasury bill
Returns the yield for a Treasury bill
Returns the depreciation of an asset for a specified or partial
period by using a declining balance method
186
XIRR
187
XNPV
188
189
YIELD
YIELDDISC
190
YIELDMAT
Returns the internal rate of return for a schedule of cash flows
that is not necessarily periodic
Returns the net present value for a schedule of cash flows that is
not necessarily periodic
Returns the yield on a security that pays periodic interest
Returns the annual yield for a discounted security; for example, a
Treasury bill
Returns the annual yield of a security that pays interest at maturity
Informational
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CELL
192
193
ERROR.TYPE
INFO
194
195
196
197
198
ISBLANK
ISERR
ISERROR
ISEVEN
ISFORMULA
199
200
201
202
203
204
205
206
207
208
209
210
ISLOGICAL
ISNA
ISNONTEXT
ISNUMBER
ISODD
ISREF
ISTEXT
N
NA
SHEET
SHEETS
TYPE
Returns information about the formatting, location, or contents
of a cell Note This is not available in Excel Web App.
Returns a number corresponding to an error type
Returns information about the current operating
environment. Note This is not available in Excel Web App.
Returns TRUE if the value is blank
Returns TRUE if the value is any error value except #N/A
Returns TRUE if the value is any error value
Returns TRUE if the number is even
Returns TRUE if there is a reference to a cell that contains a
formula
Returns TRUE if the value is a logical value
Returns TRUE if the value is the #N/A error value
Returns TRUE if the value is not text
Returns TRUE if the value is a number
Returns TRUE if the number is odd
Returns TRUE if the value is a reference
Returns TRUE if the value is text
Returns a value converted to a number
Returns the error value #N/A
Returns the sheet number of the referenced sheet
Returns the number of sheets in a reference
Returns a number indicating the data type of a value
Logical
211
212
213
214
AND
FALSE
IF
IFERROR
Returns TRUE if all of its arguments are TRUE
Returns the logical value FALSE
Specifies a logical test to perform
Returns a value you specify if a formula evaluates to an error;
otherwise, returns the result of the formula
215
IFNA
Returns the value you specify if the expression resolves to #N/A,
otherwise returns the result of the expression
216
217
218
219
NOT
OR
TRUE
XOR
Reverses the logic of its argument
Returns TRUE if any argument is TRUE
Returns the logical value TRUE
Returns a logical exclusive OR of all arguments
Lookup and Reference
220
221
ADDRESS
AREAS
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Returns a reference as text to a single cell in a worksheet
Returns the number of areas in a reference
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223
224
225
226
227
CHOOSE
COLUMN
COLUMNS
FORMULATEXT
GETPIVOTDATA
HLOOKUP
228
HYPERLINK
229
230
231
232
233
234
235
236
INDEX
INDIRECT
LOOKUP
MATCH
OFFSET
ROW
ROWS
RTD
237
238
TRANSPOSE
VLOOKUP
Chooses a value from a list of values
Returns the column number of a reference
Returns the number of columns in a reference
Returns the formula at the given reference as text
Returns data stored in a PivotTable report
Looks in the top row of an array and returns the value of the
indicated cell
Creates a shortcut or jump that opens a document stored on a
network server, an intranet, or the Internet
Uses an index to choose a value from a reference or array
Returns a reference indicated by a text value
Looks up values in a vector or array
Looks up values in a reference or array
Returns a reference offset from a given reference
Returns the row number of a reference
Returns the number of rows in a reference
Retrieves real-time data from a program that supports COM
automation (Automation: A way to work with an application's
objects from another application or development tool. Formerly
called OLE Automation, Automation is an industry standard and a
feature of the Component Object Model (COM).)
Returns the transpose of an array
Looks in the first column of an array and moves across the row to
return the value of a cell
Math & Trigonometry
239
240
241
242
243
244
245
246
247
248
249
250
251
ABS
ACOS
ACOSH
ACOT
ACOTH
AGGREGATE
ARABIC
ASIN
ASINH
ATAN
ATAN2
ATANH
BASE
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Returns the absolute value of a number
Returns the arccosine of a number
Returns the inverse hyperbolic cosine of a number
Returns the arccotangent of a number
Returns the hyperbolic arccotangent of a number
Returns an aggregate in a list or database
Converts a Roman number to Arabic, as a number
Returns the arcsine of a number
Returns the inverse hyperbolic sine of a number
Returns the arctangent of a number
Returns the arctangent from x- and y-coordinates
Returns the inverse hyperbolic tangent of a number
Converts a number into a text representation with the given radix
(base)
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CEILING
253
CEILING.MATH
254
CEILING.PRECISE
255
256
COMBIN
COMBINA
257
258
259
260
261
262
263
COS
COSH
COT
COTH
CSC
CSCH
DECIMAL
264
265
266
267
268
269
270
DEGREES
EVEN
EXP
FACT
FACTDOUBLE
FLOOR
FLOOR.MATH
271
FLOOR.PRECISE
272
273
274
GCD
INT
ISO.CEILING
275
276
277
278
279
280
281
282
283
LCM
LN
LOG
LOG10
MDETERM
MINVERSE
MMULT
MOD
MROUND
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Rounds a number to the nearest integer or to the nearest
multiple of significance
Rounds a number up, to the nearest integer or to the nearest
multiple of significance
Rounds a number the nearest integer or to the nearest multiple of
significance. Regardless of the sign of the number, the number is
rounded up.
Returns the number of combinations for a given number of objects
Returns the number of combinations with repetitions for a given
number of items
Returns the cosine of a number
Returns the hyperbolic cosine of a number
Returns the cotangent of an angle
Returns the hyperbolic cotangent of a number
Returns the cosecant of an angle
Returns the hyperbolic cosecant of an angle
Converts a text representation of a number in a given base into a
decimal number
Converts radians to degrees
Rounds a number up to the nearest even integer
Returns e raised to the power of a given number
Returns the factorial of a number
Returns the double factorial of a number
Rounds a number down, toward zero
Rounds a number down, to the nearest integer or to the nearest
multiple of significance
Rounds a number down to the nearest integer or to the nearest
multiple of significance. Regardless of the sign of the number, the
number is rounded down.
Returns the greatest common divisor
Rounds a number down to the nearest integer
Returns a number that is rounded up to the nearest integer or to
the nearest multiple of significance
Returns the least common multiple
Returns the natural logarithm of a number
Returns the logarithm of a number to a specified base
Returns the base-10 logarithm of a number
Returns the matrix determinant of an array
Returns the matrix inverse of an array
Returns the matrix product of two arrays
Returns the remainder from division
Returns a number rounded to the desired multiple
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285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
MULTINOMIAL
MUNIT
ODD
PI
POWER
PRODUCT
QUOTIENT
RADIANS
RAND
RANDBETWEEN
ROMAN
ROUND
ROUNDDOWN
ROUNDUP
SEC
SECH
SERIESSUM
SIGN
SIN
SINH
SQRT
SQRTPI
SUBTOTAL
SUM
SUMIF
SUMIFS
SUMPRODUCT
311
312
SUMSQ
SUMX2MY2
313
SUMX2PY2
314
SUMXMY2
315
316
317
TAN
TANH
TRUNC
Returns the multinomial of a set of numbers
Returns the unit matrix or the specified dimension
Rounds a number up to the nearest odd integer
Returns the value of pi
Returns the result of a number raised to a power
Multiplies its arguments
Returns the integer portion of a division
Converts degrees to radians
Returns a random number between 0 and 1
Returns a random number between the numbers you specify
Converts an Arabic numeral to Roman, as text
Rounds a number to a specified number of digits
Rounds a number down, toward zero
Rounds a number up, away from zero
Returns the secant of an angle
Returns the hyperbolic secant of an angle
Returns the sum of a power series based on the formula
Returns the sign of a number
Returns the sine of the given angle
Returns the hyperbolic sine of a number
Returns a positive square root
Returns the square root of (number * pi)
Returns a subtotal in a list or database
Adds its arguments
Adds the cells specified by a given criteria
Adds the cells in a range that meet multiple criteria
Returns the sum of the products of corresponding array
components
Returns the sum of the squares of the arguments
Returns the sum of the difference of squares of corresponding
values in two arrays
Returns the sum of the sum of squares of corresponding values in
two arrays
Returns the sum of squares of differences of corresponding values
in two arrays
Returns the tangent of a number
Returns the hyperbolic tangent of a number
Truncates a number to an integer
Statistical
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AVEDEV
319
320
AVERAGE
AVERAGEA
321
AVERAGEIF
322
AVERAGEIFS
323
324
BETA.DIST
BETA.INV
325
326
BINOM.DIST
BINOM.DIST.RANGE
327
BINOM.INV
328
329
330
331
CHISQ.DIST
CHISQ.DIST.RT
CHISQ.INV
CHISQ.INV.RT
332
333
334
CHISQ.TEST
CONFIDENCE.NORM
CONFIDENCE.T
335
336
337
338
339
CORREL
COUNT
COUNTA
COUNTBLANK
COUNTIF
340
COUNTIFS
341
COVARIANCE.P
342
COVARIANCE.S
343
344
345
346
347
DEVSQ
EXPON.DIST
F.DIST
F.DIST.RT
F.INV
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Returns the average of the absolute deviations of data points from
their mean
Returns the average of its arguments
Returns the average of its arguments, including numbers, text, and
logical values
Returns the average (arithmetic mean) of all the cells in a range
that meet a given criteria
Returns the average (arithmetic mean) of all cells that meet
multiple criteria
Returns the beta cumulative distribution
Returns the inverse of the cumulative distribution for a specified
beta distribution
Returns the individual term binomial distribution probability
Returns the probability of a trial result using a binomial
distribution
Returns the smallest value for which the cumulative binomial
distribution is less than or equal to a criterion value
Returns the cumulative beta probability density
Returns the one-tailed probability of the chi-squared distribution
Returns the cumulative beta probability density
Returns the inverse of the one-tailed probability of the chi-squared
distribution
Returns the test for independence
Returns the confidence interval for a population mean
Returns the confidence interval for a population mean, using a
Student's t distribution
Returns the correlation coefficient between two data sets
Counts how many numbers are in the list of arguments
Counts how many values are in the list of arguments
Counts the number of blank cells within a range
Counts the number of cells within a range that meet the given
criteria
Counts the number of cells within a range that meet multiple
criteria
Returns covariance, the average of the products of paired
deviations
Returns the sample covariance, the average of the products
deviations for each data point pair in two data sets
Returns the sum of squares of deviations
Returns the exponential distribution
Returns the F probability distribution
Returns the F probability distribution
Returns the inverse of the F probability distribution
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349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
F.INV.RT
F.TEST
FISHER
FISHERINV
FORECAST
FREQUENCY
GAMMA
GAMMA.DIST
GAMMA.INV
GAMMALN
GAMMALN.PRECISE
GAUSS
GEOMEAN
GROWTH
HARMEAN
HYPGEOM.DIST
INTERCEPT
KURT
LARGE
LINEST
LOGEST
LOGNORM.DIST
LOGNORM.INV
MAX
MAXA
373
374
375
MEDIAN
MIN
MINA
376
MODE.MULT
377
378
379
380
381
382
383
384
MODE.SNGL
NEGBINOM.DIST
NORM.DIST
NORM.INV
NORM.S.DIST
NORM.S.INV
PEARSON
PERCENTILE.EXC
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Returns the inverse of the F probability distribution
Returns the result of an F-test
Returns the Fisher transformation
Returns the inverse of the Fisher transformation
Returns a value along a linear trend
Returns a frequency distribution as a vertical array
Returns the Gamma value
Returns the gamma distribution
Returns the inverse of the gamma cumulative distribution
Returns the natural logarithm of the gamma, Γ(x)
Returns the natural logarithm of the gamma, Γ(x)
Returns 0.5 less than the standard normal cumulative distribution
Returns the geometric mean
Returns values along an exponential trend
Returns the harmonic mean
Returns the hypergeometric distribution
Returns the intercept of the linear regression line
Returns the kurtosis of a data set
Returns the k-th largest value in a data set
Returns the parameters of a linear trend
Returns the parameters of an exponential trend
Returns the cumulative lognormal distribution
Returns the inverse of the lognormal cumulative distribution
Returns the maximum value in a list of arguments
Returns the maximum value in a list of arguments, including
numbers, text, and logical values
Returns the median of the given numbers
Returns the minimum value in a list of arguments
Returns the smallest value in a list of arguments, including
numbers, text, and logical values
Returns a vertical array of the most frequently occurring, or
repetitive values in an array or range of data
Returns the most common value in a data set
Returns the negative binomial distribution
Returns the normal cumulative distribution
Returns the inverse of the normal cumulative distribution
Returns the standard normal cumulative distribution
Returns the inverse of the standard normal cumulative distribution
Returns the Pearson product moment correlation coefficient
Returns the k-th percentile of values in a range, where k is in the
range 0..1, exclusive
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386
PERCENTILE.INC
PERCENTRANK.EXC
387
388
389
PERCENTRANK.INC
PERMUT
PERMUTATIONA
390
391
392
PHI
POISSON.DIST
PROB
393
QUARTILE.EXC
394
395
396
397
QUARTILE.INC
RANK.AVG
RANK.EQ
RSQ
398
399
SKEW
SKEW.P
400
401
402
403
404
405
SLOPE
SMALL
STANDARDIZE
STDEV.P
STDEV.S
STDEVA
406
STDEVPA
407
STEYX
408
T.DIST
409
T.DIST.2T
410
411
T.DIST.RT
T.INV
412
413
414
T.INV.2T
T.TEST
TREND
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Returns the k-th percentile of values in a range
Returns the rank of a value in a data set as a percentage (0..1,
exclusive) of the data set
Returns the percentage rank of a value in a data set
Returns the number of permutations for a given number of objects
Returns the number of permutations for a given number of objects
(with repetitions) that can be selected from the total objects
Returns the value of the density for a standard normal distribution
Returns the Poisson distribution
Returns the probability that values in a range are between two
limits
Returns the quartile of the data set, based on percentile values
from 0..1, exclusive
Returns the quartile of a data set
Returns the rank of a number in a list of numbers
Returns the rank of a number in a list of numbers
Returns the square of the Pearson product moment correlation
coefficient
Returns the skewness of a distribution
Returns the skewness of a distribution based on a population: a
characterization of the degree of asymmetry of a distribution
around its mean
Returns the slope of the linear regression line
Returns the k-th smallest value in a data set
Returns a normalized value
Calculates standard deviation based on the entire population
Estimates standard deviation based on a sample
Estimates standard deviation based on a sample, including
numbers, text, and logical values
Calculates standard deviation based on the entire population,
including numbers, text, and logical values
Returns the standard error of the predicted y-value for each x in
the regression
Returns the Percentage Points (probability) for the Student tdistribution
Returns the Percentage Points (probability) for the Student tdistribution
Returns the Student's t-distribution
Returns the t-value of the Student's t-distribution as a of the
probability and the degrees of freedom
Returns the inverse of the Student's t-distribution
Returns the probability associated with a Student's t-test
Returns values along a linear trend
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416
417
418
TRIMMEAN
VAR.P
VAR.S
VARA
419
VARPA
420
421
WEIBULL.DIST
Z.TEST
Returns the mean of the interior of a data set
Calculates variance based on the entire population
Estimates variance based on a sample
Estimates variance based on a sample, including numbers, text,
and logical values
Calculates variance based on the entire population, including
numbers, text, and logical values
Returns the Weibull distribution
Returns the one-tailed probability-value of a z-test
Text
422
ASC
Changes full-width (double-byte) English letters or katakana within
a character string to half-width (single-byte) characters
423
424
425
426
427
428
BAHTTEXT
CHAR
CLEAN
CODE
CONCATENATE
DBCS
Converts a number to text, using the ß (baht) currency format
Returns the character specified by the code number
Removes all nonprintable characters from text
Returns a numeric code for the first character in a text string
Joins several text items into one text item
Changes half-width (single-byte) English letters or katakana within
a character string to full-width (double-byte) characters
429
430
431
432
433
434
435
436
DOLLAR
EXACT
FIND, FINDBs
FIXED
LEFT, LEFTBs
LEN, LENBs
LOWER
MID, MIDBs
437
438
439
440
441
442
443
444
445
446
447
NUMBERVALUE
PHONETIC
PROPER
REPLACE, REPLACEBs
REPT
RIGHT, RIGHTBs
SEARCH, SEARCHBs
SUBSTITUTE
T
TEXT
TRIM
Converts a number to text, using the $ (dollar) currency format
Checks to see if two text values are identical
Finds one text value within another (case-sensitive)
Formats a number as text with a fixed number of decimals
Returns the leftmost characters from a text value
Returns the number of characters in a text string
Converts text to lowercase
Returns a specific number of characters from a text string starting
at the position you specify
Converts text to number in a locale-independent manner
Extracts the phonetic (furigana) characters from a text string
Capitalizes the first letter in each word of a text value
Replaces characters within text
Repeats text a given number of times
Returns the rightmost characters from a text value
Finds one text value within another (not case-sensitive)
Substitutes new text for old text in a text string
Converts its arguments to text
Formats a number and converts it to text
Removes spaces from text
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UNICHAR
449
UNICODE
450
451
UPPER
VALUE
Returns the Unicode character that is references by the given
numeric value
Returns the number (code point) that corresponds to the first
character of the text
Converts text to uppercase
Converts a text argument to a number
User defined that are installed with add-ins
452
453
CALL
EUROCONVERT
Calls a procedure in a dynamic link library or code resource
Converts a number to euros, converts a number from euros to a
euro member currency, or converts a number from one euro
member currency to another by using the euro as an intermediary
(triangulation)
Returns the register ID of the specified dynamic link library (DLL) or
code resource that has been previously registered
454
REGISTER.ID
455
SQL.REQUEST
Connects with an external data source and runs a query from a
worksheet, then returns the result as an array without the need
for macro programming
453
454
ENCODEURL
FILTERXML
Returns a URL-encoded string
Returns specific data from the XML content by using the specified
XPath
455
WEBSERVICE
Web
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Using Excel to Detect Fraud
The 171 Most Relevant and Important Functions to CPAs
(According to Carlton’s best guess)
The numbers in parenthesis correspond to the order
in which each function appears on the All Functions worksheet
Open an Excel file Containing Function Examples:
http://www.ASAResearch.com/web/functions.xlsx
The goal of this summary is to help CPAs focus on learning the most relevant and important
functions first, without having to waste time wading through all 455 functions. Excel 2013
provides 455 functions, but in most cases only 37.5% of them are relevant and important to CPAs.
Consider, how often do you expect to use the CRITBINOMIALDIST function? How often will the
IMAGINARY function have relevance to your work? When did any CPA ever calculate depreciation
using the SYD method (other than on a CPA example question). The reality is that CPAs don’t
have to know all of the functions to master Excel, you’ll conquer Excel just by learning 37.5% of
the included functions.
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
IF (213) - Specifies a logical test to perform.
SUM (307) - Adds its arguments.
SUBTOTAL (306) - Returns a subtotal in a list or database.
SUMIF (308) - Adds the cells specified by a given criteria.
COUNT (336) - Counts how many numbers are in the list of arguments.
COUNTA (337) - Counts how many values are in the list of arguments.
AVERAGE (319) - Returns the average of its arguments.
COUNTBLANK (338) - Counts the number of blank cells within a range.
COUNTIF (339) - Counts the number of cells within a range that meet the given criteria.
VALUE (451) - Converts a text argument to a number.
TEXT (446) - Formats a number and converts it to text.
VLOOKUP (238) - Looks in the first column of an array and moves across the row to
return the value of the indicated cell.
HLOOKUP (227) - Looks in the top row of an array and returns the value of the indicated
cell.
TWO WAY LOOKUP – Using both VLOOKUP and HLOOKUP together.
LOOKUP (231) - Looks up values in a vector or array.
MATCH (232) - Looks up values in a reference or array.
TRIM (447) - Removes spaces from text.
PROPER (439) - Capitalizes the first letter in each word of a text value.
LOWER (435) - Converts text to lowercase.
UPPER (450) - Converts text to uppercase.
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21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
47.
48.
49.
50.
51.
52.
53.
54.
55.
LEFT, LEFTBs (433) - Returns the leftmost characters from a text value.
RIGHT, RIGHTBs (442) - Returns the rightmost characters from a text value.
MID, MIDBs (436) - Returns a specific number of characters from a text string starting at
the position.
FIND, FINDBs (431) - Finds one text value within another (case-sensitive).
SUBSTITUTE (444) - Substitutes new text for old text in a text string.
LEN, LENBs (434) - Returns the number of characters in a text string.
REPLACE, REPLACEBs (440) - Replaces characters within text.
CONCATENATE (427) - Joins several text items into one text item.
CLEAN (425) - Removes all nonprintable characters from text.
NOW (71) - Returns the serial number of the current date and time.
TODAY (75) - Returns the serial number of today's date.
DATE (58) - Returns the serial number of a particular date.
MONTH (68) - Converts a serial number to a month.
DAY (60) - Converts a serial number to a day of the month.
YEAR (80) - Converts a serial number to a year.
WEEKDAY (76) - Converts a serial number to a day of the week.
ROUND (295) - Rounds a number to a specified number of digits.
ROUNDDOWN (296) - Rounds a number down, toward zero.
ROUNDUP (297) - Rounds a number up, away from zero.
MAX (371) - Returns the maximum value in a list of arguments.
DMAX (50) - Returns the maximum value from selected database entries.
MIN (374) - Returns the minimum value in a list of arguments.
DMIN (51) - Returns the minimum value from selected database entries.
MEDIAN - Returns the median of the given numbers.
MODE - Returns the most common value in a data set.
PERCENTILE (25) - Returns the k-th percentile of values in a range.
PERCENTRANK (26) - Returns the percentage rank of a value in a data set.
PMT (171) - Returns the periodic payment for an annuity.
NPV (165) - Returns the net present value of an investment based on a series of periodic
cash flows.
DSUM (55) - Adds the numbers in the field column of records in the database that match
the criteria.
DCOUNT (47) - Counts the cells that contain numbers in a database.
DCOUNTA (48) - Counts nonblank cells in a database.
AND (211) - Returns TRUE if all of its arguments are TRUE.
OR (217) - Returns TRUE if any argument is TRUE.
CHOOSE (222) - Chooses a value from a list of values.
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57.
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63.
64.
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67.
68.
69.
70.
71.
72.
73.
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75.
76.
77.
78.
79.
80.
81.
82.
83.
84.
85.
86.
87.
88.
TIME (73) - Returns the serial number of a particular time.
FV (155) - Returns the future value of an investment.
IRR (159) - Returns the internal rate of return for a series of cash flows.
YIELD (188) - Returns the yield on a security that pays periodic interest.
CELL (191) - Returns information about the formatting, location, or contents of a
cell. Note
INFO (193) - Returns information about the current operating environment. Note This
is not avail.
ERROR.TYPE (192) - Returns a number corresponding to an error type.
ISBLANK (194) - Returns TRUE if the value is blank.
ISNA (200) - Returns TRUE if the value is the #N/A error value.
GETPIVOTDATA (226) - Returns data stored in a PivotTable report.
HYPERLINK (228) - Creates a shortcut or jump that opens a document stored on a
network server, an in.
TRANSPOSE (237) - Returns the transpose of an array.
ABS (239) - Returns the absolute value of a number.
RAND (292) - Returns a random number between 0 and 1.
RANDBETWEEN (293) - Returns a random number between the numbers you specify.
REPT (441) - Repeats text a given number of times.
SLN (180) - Returns the straight-line depreciation of an asset for one period.
SYD - Returns the sum-of-years' digits depreciation of an asset for a specified period.
DDB (149) - Returns the depreciation of an asset for a specified period by using the
double-declining balance method.
DGET (49) - Extracts from a database a single record that matches the specified criteria.
ADDRESS (220) - Returns a reference as text to a single cell in a worksheet.
AGGREGATE (244) - Returns an aggregate in a list or database.
FORECAST (352) - Returns a value along a linear trend.
GROWTH (361) - Returns values along an exponential trend.
LARGE (366) - Returns the k-th largest value in a data set.
NOT (216) - Reverses the logic of its argument.
OFFSET (233) - Returns a reference offset from a given reference.
PEARSON (383) - Returns the Pearson product moment correlation coefficient.
PV (176) - Returns the present value of an investment.
RATE (177) - Returns the interest rate per period of an annuity.
SEARCH, SEARCHBs (443) - Finds one text value within another (not case-sensitive).
SMALL (401) - Returns the k-th smallest value in a data set.
XIRR (186) - Returns the internal rate of return for a schedule of cash flows that is not
necessarily.
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90.
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95.
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97.
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108.
109.
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111.
112.
113.
114.
115.
116.
117.
118.
119.
120.
XOR (219) - Returns a logical exclusive OR of all arguments.
AVERAGEIF (321) - Returns the average (arithmetic mean) of all the cells in a range that
meet a give.
AVERAGEIFS (322) - Returns the average (arithmetic mean) of all cells that meet multiple
criteria.
COLUMN (223) - Returns the column number of a reference.
COLUMNS (224) - Returns the number of columns in a reference.
CONVERT (95) - Converts a number from one measurement system to another.
COUNTIFS (340) - Counts the number of cells within a range that meet multiple criteria.
DATEVALUE (59) - Converts a date in the form of text to a serial number.
DECIMAL (263) - Converts a text representation of a number in a given base into a
decimal number.
DOLLAR (429) - Converts a number to text, using the $ (dollar) currency format.
EXACT (430) - Checks to see if two text values are identical.
FORMULATEXT (225) - Returns the formula at the given reference as text.
HOUR (65) - Converts a serial number to an hour.
INDEX (229) - Uses an index to choose a value from a reference or array.
ISERR (195) - Returns TRUE if the value is any error value except #N/A.
ISERROR (196) - Returns TRUE if the value is any error value.
ISFORMULA (198) - Returns TRUE if there is a reference to a cell that contains a formula.
KURT (365) - Returns the kurtosis of a data set.
MINUTE (67) - Converts a serial number to a minute.
NA (207) - Returns the error value #N/A.
PPMT (172) - Returns the payment on the principal for an investment for a given period.
ROW (234) - Returns the row number of a reference.
ROWS (235) - Returns the number of rows in a reference.
RSQ (397) - Returns the square of the Pearson product moment correlation coefficient.
SHEET (208) - Returns the sheet number of the referenced sheet.
SHEETS (209) - Returns the number of sheets in a reference.
SKEW (398) - Returns the skewness of a distribution.
SKEW.P (399) - Returns the skewness of a distribution based on a population: a
characterization of t.
SUMIFS (309) - Adds the cells in a range that meet multiple criteria.
T (445) - Converts its arguments to text.
TYPE (210) - Returns a number indicating the data type of a value.
WEEKNUM (77) - Converts a serial number to a number representing where the week
falls numerically with a year.
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143.
144.
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147.
148.
149.
WORKDAY (78) - Returns the serial number of the date before or after a specified
number of workdays.
XNPV (187) - Returns the net present value for a schedule of cash flows that is not
necessarily periodic.
EFFECT (154) - Returns the effective annual interest rate.
INT (273) - Rounds a number down to the nearest integer.
INTERCEPT (364) - Returns the intercept of the linear regression line.
RANK (29) - Returns the rank of a number in a list of numbers.
RRI (179) - Returns an equivalent interest rate for the growth of an investment.
SIGN (301) - Returns the sign of a number.
SLOPE (400) - Returns the slope of the linear regression line.
CEILING (252) - Rounds a number to the nearest integer or to the nearest multiple of
significance.
CEILING.MATH (253) - Rounds a number up, to the nearest integer or to the nearest
multiple of significance.
CHAR (424) - Returns the character specified by the code number.
CODE (426) - Returns a numeric code for the first character in a text string.
DAVERAGE (46) - Returns the average of selected database entries.
DAYS (61) - Returns the number of days between two dates.
FALSE (212) - Returns the logical value FALSE.
FIXED (432) - Formats a number as text with a fixed number of decimals.
FLOOR.MATH (270) - Rounds a number down, to the nearest integer or to the nearest
multiple of significance .
IFERROR (214) - Returns a value you specify if a formula evaluates to an error;
otherwise, returns t.
IFNA (215) - Returns the value you specify if the expression resolves to #N/A, otherwise
returns the.
INDIRECT (230) - Returns a reference indicated by a text value.
IPMT (158) - Returns the interest payment for an investment for a given period.
ISNONTEXT (201) - Returns TRUE if the value is not text.
ISNUMBER (202) - Returns TRUE if the value is a number.
MAXA (372) - Returns the maximum value in a list of arguments, including numbers,
text, and logical .
MINA (375) - Returns the smallest value in a list of arguments, including numbers, text,
and logical.
N (206) - Returns a value converted to a number.
NETWORKDAYS (69) - Returns the number of whole workdays between two dates.
RECEIVED (178) - Returns the amount received at maturity for a fully invested security.
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151.
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159.
160.
161.
162.
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164.
165.
166.
167.
168.
169.
170.
171.
172.
SECOND (72) - Converts a serial number to a second.
SUMPRODUCT (310) - Returns the sum of the products of corresponding array
components.
TREND (414) - Returns values along a linear trend.
TRUE (218) - Returns the logical value TRUE.
ACCRINT (136) - Returns the accrued interest for a security that pays periodic interest.
ACCRINTM (137) - Returns the accrued interest for a security that pays interest at
maturity.
ISPMT (160) - Calculates the interest paid during a specific period of an investment.
ISTEXT (205) - Returns TRUE if the value is text.
LINEST (367) - Returns the parameters of a linear trend.
CUBEKPIMEMBER (39) - Returns a key performance indicator (KPI) property and displays
the KPI name in.
DELTA (99) - Tests whether two values are equal.
EVEN (265) - Rounds a number up to the nearest even integer.
ISEVEN (197) - Returns TRUE if the number is even.
ISODD (203) - Returns TRUE if the number is odd.
ISOWEEKNUM (66) - Returns the number of the ISO week number of the year for a given
date.
LOGEST (368) - Returns the parameters of an exponential trend.
ODD (286) - Rounds a number up to the nearest odd integer.
PRODUCT (289) - Multiplies its arguments.
QUOTIENT (290) - Returns the integer portion of a division.
STDEVP (31) - Calculates standard deviation based on the entire population.
TBILLEQ (182) - Returns the bond-equivalent yield for a Treasury bill.
TBILLYIELD (184) - Returns the yield for a Treasury bill.
TIMEVALUE (74) - Converts a time in the form of text to a serial number.
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Bio for J. Carlton Collins, CPA
ASA Research Carlton@ASAResearch.com 770.842.5902
J. Carlton Collins, CPA is a Certified Public Accountant with experience in technology,
tax, auditing, accounting systems, financial reporting, and bond financing. He is an
author, lecturer, and technology & accounting systems consultant. He has published
books, articles, and web pages and is the author of the monthly technology Q&A column
for the Journal of Accountancy. As a public speaker, Mr. Collins has delivered more than
2,000 lectures in 44 states and 5 countries addressing more than 500,000 CPAs and
business professionals. As a consultant, Mr. Collins has assisted 275+ large and small
companies with the selection and implementation of accounting systems. Mr. Collins has a Bachelor’s degree in
Accounting from the University of Georgia, is a 25+ year member of the American Institute of CPAs and the Georgia
Society of CPAs.
Summary of Selected Positions, Awards & Accomplishments:
1. Honored as one of the CPA Industries Top 25 Thought Leaders by CPA Technology Advisor Magazine
2. Author of the monthly Technology Q&A column for the Journal of Accountancy.
3. Recipient of the AICPA’s Lifetime Technical Contribution to the CPA Profession Award.
4. Chairman of the Southeast Accounting Show - the South’s largest CPA event.
5. Recipient of the Tom Radcliff Outstanding Discussion Leader Award.
6. Named “Top Ten CPA Technologists” by Accounting Technologies Magazine (multiple years).
7. Named “Top 100 Most Influential CPAs ” by Accounting Technologies Magazine (multiple years).
8. Has personally delivered over 2,000 technology lectures around the world.
9. Recipient of the Outstanding Discussion Leader Award from the Georgia Society of CPAs.
10. Lead author for PPC's Guide to Installing Microcomputer Accounting Systems.
11. Has installed accounting systems for more than 200 companies.
12. Chairperson of the AICPA Technology Conference.
13. Recipient of the ACCPAC Partner of the Year Award.
14. Determined by SAP to be one of the country's "Top Ten Most Influential ERP Systems Consultants".
15. Has delivered keynote and session lectures at dozens of accounting software conferences.
16. Sworn in as a Certified Public Accountant on September 18, 1985.
17. Member of the American Institute of CPAs since 1985.
18. Member of the Georgia Society of CPAs since 1982.
As an auditor, Mr. Collins has audited businesses in the areas of health care, construction, distribution, automobile
dealerships, insurance, manufacturing, and general business. Mr. Collins' tax experience includes corporate,
individual, partnership, fiduciary, and estate tax planning work. In the area of finance, Mr. Collins has prepared (or
assisted in preparing) feasibility studies and financial forecasts for nearly 300 projects seeking more than $3 billion
in startup capital. Mr. Collins is familiar with bond issues, Medicare and Medicaid reimbursement, and conventional
financing matters. In 1992, Mr. Collins contributed and demonstrated more than 500 pages of suggested design
improvements to the Microsoft Excel development team of programmers - and many of those improvements are
found in Excel today.
At the University of Georgia, Mr. Collins was elected President of the Phi Eta Sigma Honor Society, was initiated into
the BIFTAD Honor Society, served three years in the Judicial Defender/Advocate program, and was a member of
Alpha Tau Omega fraternity. At Glynn Academy High School, Mr. Collins was Senior Class President, Class
Valedictorian (1 of 6), and received a principle nomination to Annapolis Naval Academy. Mr. Collins has been married
for 27 years and has two children. He devotes his leisure time to family, travel, tennis, fishing, snow skiing, and riding
motorcycles (both dirt and street). Mr. Collins is president of his homeowners association, participates in the
Gwinnett Clean and Beautiful program, and volunteers for Cooperative Ministries food drive.
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