Error theory and regression analysis
35
Carl von Ossietzky University Oldenburg – Faculty V - Institute of Physics
Module Introductory laboratory course physics – Part I
Error Theory and Regression Analysis
Keywords:
Measurand, measured value, result of a measurement, uncertainty, Systematic and random error,
absolute and relative error, frequency distribution, density function, Gaussian error curve, mean,
standard deviation, variance, root mean square error, maximum error, law of error propagation, linear
regression.
References:
/1/ BIPM 1: “Evaluation of measurement data — Guide to the expression of uncertainty in measurement” (GUM),
2008
http://www.bipm.org/utils/common/documents/jcgm/JCGM_100_2008_E.pdf
/2/ DIN 1319-3 2: “Grundlagen der Messtechnik - Teil 3: Auswertung von Messungen einer einzelnen Messgröße,
Messunsicherheit“, 1996
/3/ DIN 1319-4: “Grundlagen der Messtechnik - Teil 4: Auswertung von Messungen, Messunsicherheit“, 1999
/4/ NIST 3 Technical Note 1297: “Guidelines for Evaluating and Expressing the Uncertainty of NIST
Measurement Results”, 1994 (http://physics.nist.gov/Pubs/guidelines/TN1297/tn1297s.pdf)
/5/ TAYLOR, J. R.: “Fehleranalyse“, VCH Verlagsgesellschaft mbH, Weinheim
/6/ YOUNG, H. D.: “Statistical Treatment of Experimental Data“, McGraw-Hill, New York among others
1
Introduction
An experiment to determine the gravitational acceleration of the earth g is performed in a lecture hall. A
ball is suspended in the air by a magnetic holder. After activation of the magnet, the ball drops onto a
platform. It requires the time t for the fall through the distance s. By measuring the measurands s and t it is
possible to determine the measurand g:
(1)
g=
2s
t2
The apparatus is constructed in such a manner that a flash of light is emitted upon the release of the ball
and its impact on the platform. The time t between the two light flashes is measured by the students in the
lecture hall using a stop watch. Nobody would expect that everyone will measure the same time. The
individual measured values will deviate from each other. This is, for one, caused by the different reaction
times of the students, and another thing to consider is the variation in drift and calibration among the
individual stop watches. Likewise, the measured values for the distance s will differ when measured by
several students since the positioning and reading of the measuring rod will vary from person to person. In
addition, the measuring rod’s calibration accuracy is limited as well.
This leads to the following questions:
(1)
(2)
(3)
Which values of s and t should be used for the determining g by Eq. (1) ?
How can the facts that the individual measured values for s and t deviate from each other, and the
limited accuracy of the measuring instruments be accounted for?
How good is the value of g obtained from the measured values?
The answers to these questions are:
1
2
3
BIPM: Bureau International des Poids et Mesures
DIN: Deutsches Institut für Normung e.V.
NIST: National Institute of Standards and Technology des United States Department of Commerce - Technology
Administration.
36
To (1): A single result of the measurement must be derived from the individual measured values by fixed
rules. The results of the measurement for s and t are inserted into Eq. (1), thus providing a result of
the measurement for g.
To (2): Uncertainties of the results of measurement for s and t have to be calculated according to fixed
rules. The uncertainties provide a statistical measure for the deviations among the individual
measured values. They (the measuring inaccuracies) are defined in a manner that an additional
measurement of s or t will yield a result within the interval result of the measurement ± uncertainty
with a well-defined probability.
To (3): From the results of measurement and uncertainties for s and t an uncertainty for g must be calculated
according to fixed rules. A further measurement of g using the same measuring procedure will then
yield a result within the interval result of the measurement ± uncertainty with a well-defined
probability. Hence, both values together compose the full result of the measurement.
The rules mentioned above are internationally valid. They are described in great detail for all conceivable
applications in norms and manuals. (e.g. /1/ - /4/). In addition, a number of books dedicated to this subject
are available (e.g. /5/ and /6/). It is out of scope of this script to reiterate these rules in full detail. We will
hence restrict ourselves to present some basics and provide the tools needed in the laboratory course for
calculating results of measurements and uncertainties.
2
Direct and Indirect Measurement
In the example considered above, the measurands s and t may be measured directly, that is, by using a stop
watch and measuring rod. One speaks of a direct measurement in such a case. The value g in the example
is measured indirectly, that is by obtaining the result of measurement for g from the results of measurement
of s and t. In such a case one speaks of an indirect measurement.
3
Notation
Following /2/, in the context of the measurement of a measurand the terms result of a measurement and
uncertainty should be used. This terminology has, however, not become commonly used as of today in
physics laboratories. More often the term “error” is used instead of uncertainty. For this reason, it is
common to speak of “error analysis” instead of calculation of uncertainties, or about “error bars” instead
of bars of uncertainty. In this text we will use both terms “error” and “uncertainty” (of a measurement).
4
Possible Kinds of Errors
4.1
Systematic Errors
Systematic errors may result from imperfect measuring instruments, from unavoidable influences of the
surroundings on the measurement, or also from an unsuitable measurement method. This is illustrated by
some examples from the lab course:
(1) Imperfect measuring instruments: These are, e.g., an oscilloscope with a poorly adjusted time base
unit, a multimeter with residual deviation, poorly adjusted electronic scales etc. The unpleasant thing
about these faults is that one does not always notice them during the measurement. On the contrary: the
value obtained (e.g., 27.5 µs, 147 Ω, 5.389 g) feigns an accuracy which one expects from these types
of instrument, so there is no reason to doubt the results.
(2) Influence of the surroundings: An example for this is the temperature dependence of measuring
instruments. Normally, these dependencies are known quantitatively. They can be taken from manuals
and considered for the analysis.
(3) Unsuitable measurement methods: When one tries to determine the mass of a magnet using an
electronic scale, one soon notices that the measured result is obviously absurd. The measurement
method is unsuitable, because the magnetic field affects the mechanism of the scale; one needs to use
another type of scale. It is considerably more difficult to judge whether the current measured in an
37
electric circuit is negligibly affected by the circuit wiring and the internal resistance of the device. In
this case it is not so obvious at first sight, whether the measured value is ”right“ or ”wrong“. Hence,
one can, in general, not depend on simply hoping to notice when a wrong method of measurement was
used. Rather, it is necessary to carefully consider the appropriate measurement method to be applied
when planning the experiment.
Systematic errors cannot be excluded entirely. They influence the results in a very certain way - there is a
“system“ in the errors. This means that one cannot reduce their influence on the result even by frequently
repeating the measurement. However, if the extent of the systematic error (e.g. residual deflection of an
ohmmeter, the temperature course of an amplifier or the calibration error of a pressure sensor) is known,
one can account for it in the result.
4.2
Random Errors
Random errors affect the result of a measurement in an unpredictable and uncontrollable (which means a
purely random) way. Causes of random errors, which may occur during a lab course may, for example, be
the following:
(1) The randomness of a natural process such as, e.g., radioactive decay or emission of photons from a
light source. For example, it may result in an accidental fluctuation of the number of detected events
during a measuring time t.
(2) The stop watch is pressed too early or too late according to the individual reaction time.
(3) The measuring rod or the caliper gauge from which sometimes a value too small or too large is read
off.
(4) The electronic noise of a measuring amplifier, which causes fluctuations in the output voltage.
Random errors always lead to the fact that the measured result deviates from the “true“ value in one direction or the other (cf. Chap. 5 for the term “true“ value). If the measurement is repeated several times, the
deviations in both directions balance each other. If this was not true, the observed errors would not be purely
random.
From the statements above the following conclusions may be drawn: if there are no experiences with a
certain measuring method, a single measured value does not, in principle, provide any information. The
measured value may randomly deviate more or less from the ”true“ value. Only with frequent repetition of
the measurement or previous experience with the measuring method one can get an idea of the value about
which the individual measured values vary and it becomes possible to evaluate the validity of such a
measured value. In the following chapters, these relationships are described quantitatively by means of
formulas.
5
The Frequency Distribution of Measured Values
Let us assume that a measurand, e.g. the time t which a body takes to get from A to B, was measured N
times 4 . Thus, N measurement values were obtained which deviate from each other governed by the laws
of randomness. The question is: Which of these values is closest to the ”true“ value?
In order to solve that question, one must examine whether certain values occur more frequently than others,
and if so, which ones. Because one may rightly expect that the most frequent, i.e. most probable, values are
closest to the ”true“ value. For this purpose, the N measurement values, ranging between tmin and tmax, are
grouped in j classes with a class range ∆t. The range of measured values
(2)
4
tmin + ( i − 1) ∆t ≤ t < tmin + i∆t
The following considerations apply to every physical measurand. The quantity t (time) only serves as an example
here.
38
is assigned to the class i (i = 1, 2,..., j) 5.. Each class i is assigned a time ti , which corresponds to the centre
of the respective time interval. Now, for each class i, the number of values per ∆t, n(ti) belonging to this
classis plotted over the corresponding time as a bar. By this method, one obtains a bar diagram from which
may be gathered how often the measured values have occurred (Fig. 1).
The envelope curve of this diagram, n(ti), is called distribution graph of measured values. According to its
definition the area below the distribution graph always equals the total number of measured values, N.
Fig. 1:
Distribution graph of measured
values. The width of a time interval
(bar) is ∆t. (German “Fläche” = area).
Fig. 2:
Distribution graph (density function) of the
GAUSS distribution or normal distribution
(GAUSS curve).
Question 1:
- What is the (very simple!) relationship between the rates of a measured value ∆t, n(ti), and the number
of measured values within a class, m(ti)? What is the unit of the quantity n(ti) in the chosen example?
- What is the equation for calculating N from the distribution graph n(ti)?
Past experience has shown, and the theory established by CARL FRIEDRICH GAUß (Fig. 3) substantiates that
for N → ∞ and ∆t → 0 (and thus ti → t) the distribution graph n(t) for values that have been obtained
independently of each other and are affected by random errors has a quite characteristic form: The form of
a Gaussian bell-shaped curve or, briefly, Gaussian (Fig. 2). In those cases it is common practice to say
that the measured values are Gaussian distributed or normally distributed.
Fig. 3:
5
6
CARL FRIEDRICH GAUß (1777 - 1855) 6
An example for this: Let the measured values range between tmin = 20.4 s and tmax = 22.3 s, the precision of the watch reading
be 0.1 s. The measured values are hence divided into j= (22.3 - 20.4)/0.1 = 19 classes, each of which represents a time interval
of ∆t = 0.1 s width
Image source: GELLERT, W. et al. [Eds.]: „Kleine Enzyklopädie Mathematik“, VEB Bibliographisches Institut, Leipzig, 1969.
39
The area under the Gaussian again equals the total number of measured values, N. In general it is normalized
to 1. As explained below, this means that the probability of finding a value in the entire range of values
between -∞ and +∞ equals 1 7 .
The course of the Gaussian normalized to the area 1 is given by:
(3)
1
n (t ) =
e
σ 2π
−
( t − t )2
∞
2σ 2
with
∫
n(t )dt = 1
−∞
where t is the mean and σ the standard deviation of the Gaussian, the square of the standard deviation,
σ2, is called variance. At t = t ± σ the Gaussian has its inflection point. The quantities t and σ are of
great practical importance:
- The mean t is the value where n(t) has a maximum. This is the most frequently occurring value in a
series of infinitely many measurements. Thus, it represents the most probable result of the measurement.
In other words: a series of measurements never yields a true value, but always a most probable value.
- The standard deviation σ is a measure of the scattering of measured values around the mean t . The
greater the scattering, the larger σ is, the larger the width of the distribution graph becomes (while the
area remains unchanged), and the less single values differ from the other values.
Question 2:
- Calculate and sketch n(t) with the aid of Matlab according to Eq. (3) in the time interval
121.5 s ≤ t ≤ 123.5 s for t = 122.5 s as well as a) σ = 0.1 s and b) σ = 0.2 s. Show both curves in one
diagram (Matlab -Command hold on). Eq. (3)is written in the following form in Matlab:
n = (1/(sigma*sqrt(2*pi)))*exp(- ((t – t_quer).^2)/(2*sigma^2))
Let us pretend that we have performed our experiment such that the conditions N → ∞ and ∆t → 0 are
almost fulfilled - so that the distribution graph of our measured values is approximately given by a Gaussian
according to Eq. (3). Then through integration of n(t) one can calculate (hence, one does not need to count!),
how many values are found in the time interval [ t − σ , t + σ ] , i.e. within the range t ± σ .
We know that all N values must be in the time interval [-∞, +∞]. Due to normalization of the area under
the Gaussian to 1 (cf. Eq. (3)) this means:
(4)
+∞
∫−∞ 𝑛𝑛(𝑡𝑡)𝑑𝑑𝑑𝑑 = 1  𝑁𝑁  100% of all measured values.
For the interval [ t - σ, t + σ] we obtain:
t +σ
(5)
∫σ
t−
t +σ
−
1
n(t )dt = ∫
e
t − σ σ 2π
( t − t )2
2σ 2
dt ⋲0,683  0,683 N  68,3% of all measured values.
 Those who want to repeat that calculation should be warned: The integral over the Gaussian according
to Eq. (3) can only be solved numerically and not analytically! It is given in Table 2 (Chap. 11.5).
7
In the observed example, the time t is the physical quantity, its real value range can only be within the interval 0 ≤ t ≤ ∞. Hence,
it appears to be formally wrong or at least nonsensical to extend the range of values to - ∞. However, in practice the part of the
integral of Eq. (3) is so small within the range - ∞ ≤ t < 0 (≈ 0) that it can be neglected. This is why the limits of the range of
values are determined to be ± ∞ for reasons of mathematical simplification.
40
Fig. 4:
Partial areas below a Gaussian with a total area normalized to 1. Top: Partial area in the range
Bottom: Partial area in the range
t ± σ.
t ± 2σ.
If the distribution graph of the measured values is represented by a Gaussian (a fact from which we mostly
start in practice), always about 68.3% of all values are found within the interval t ± σ (Fig. 4). For the
range t ± 2σ , we always obtain a percentage of about 95.5 % (cf. Fig. 4) and for the range t ± 3σ always
a percentage of about 99.7 %. In the laboratory jargon we usually say: 68 % of all measured values are
found within the 1σ range around the mean, 95 % in the 2σ range, and 99 % in the 3σ range around the
mean.
Of course, the conditions N → ∞ and ∆t → 0 cannot be fulfilled in practice. Thereby the interval is
increased, in which we find, e.g., 68.3 % of all measured values. In this case, t ± σ has to be replaced by
t ± pσ in which the value of the quantity p ≥ 1 depends on N and can be calculated by statistical methods
(e.g. p = 1.32 for N = 3, p = 1.15 for N = 5, p = 1.06 for N = 10 and p → 1 for N → ∞). However, for the
evaluation of measurements within the laboratory course we won't take this into account.
6
Mean and Standard Deviation
In the preceding chapter it was detailed that, respecting the assumptions one can make the following
statement about a single measurement (one measured value) from a series of measurements:
 The result of a single measurement is within the range t ± σ with a probability of 68 %.
For practical work the question arises of how to determine t and σ. Since neither the condition N → ∞ nor
the condition ∆t → 0 can be met in a real experiment, one needs to find out how one can calculate the best
assessed value, briefly: calculate the optimum for t and σ from a finite number of values (a so-called
sample). We will dispense of the theoretical deduction for calculating the optimum values and state the
results.
6.1
Mean Value
If a measurand, e.g. the time t, is measured N times, the optimum for the mean t , resulting from the case
N → ∞, is the arithmetic average of the measured values ti:
(6)
t =
1
N
N
∑ ti
i= 1
41
6.2
Standard Deviation for a Single Measurement
The optimum for the standard deviation σ of the single measurement is:
(7)
=
σ
 1 N

( ti − t
 N − 1 i∑
=1


)2 

This can be explained: The standard deviation for a single measurement is a measure for the deviation of
measured values ti around the mean t . The deviation 8 of a single measured value ti from t is given by the
difference (ti - t ) (cf. Fig. 5). If the arithmetic average of these differences was taken as a measure of
deviation, we would obtain zero as a direct consequence of the definition of the average, because positive
and negative differences would cancel each other out. The information about the existing deviation of
measured values would thus get lost. In order to prevent such a loss of information, the differences are
squared:
( ti − t )
2
This turns all quantities into positive numbers. Then, the arithmetic average of these squares and finally the
root of this value is calculated.
The fact that the values are not divided by N but by N – 1 for calculating the arithmetic average can be
justified by a detailed statistical analysis, which particularly goes into differences between random sample
and parent population. However, we will not go into further detail here, since the deviation between 1/N
and 1/(N-1) is infinitesimal for high N.
14
t5 - t > 0
12
ti / s
10
8
t
t15 - t < 0
6
4
2
0
0
5
10
15
20
25
30
35
i
Fig. 5:
Illustration of the standard deviation: 32 measured values ti of time t are plotted over the number of
measurement i. t is the average of ti. For i = 5 and i = 15, the deviations have been drawn exemplarily into
the diagram between ti and
t.
The standard deviation σ of the single measurement is often termed error (uncertainty) of a single measurement, or, in accordance with Eq. (7), root-mean-square error (rms error).
6.3
Standard Deviation of the Mean
In practice the standard deviation σ of the single measurement is oftentimes not the fundamental quantity.
It is not very interesting to know with which probability a single measured value lies within the range t
± σ. Much more important is the question of how reliable and reproducible is the mean t , which was found
8
According to /2/ this value is called uncertainty of measurement.
42
with a series of measurements and which represents the result of a measurement. In other words: with which
probability would the measured result of another measurement series, i.e., a second mean, be found in a
given interval around the first one? In order to answer that question, a statement about the standard
deviation of the mean σ t is required analogous to the standard deviation of the single measurement.
Assuming we repeated a series of measurements consisting of N single measured values M times, so that
M means t i (i = 1, 2,..., M) are obtained. It can be shown that for M → ∞ the distribution graph of these
means of the measurement series would again yield a Gaussian curve with the standard deviation σ t .
In practice one does not wish to repeat the measurement series M times in order to determine the standard
deviation of the mean, σ t . Instead, the aim is to determine the optimum for σ t from one series of
measurements with N measured values). That optimum results in:
(8)
=
σt
σ
=
N
N


1
2

ti − t ) 
(
∑
 N ( N − 1) i = 1



Now the following probabilistic statement about the mean t of the measurement series representing the
result of the measurement can be made.
 The result of measurement of another measurement series is in the range t ± σ t with a probability
of about 68 %.
Further, it holds that:
 The standard deviation of the mean σ t is the uncertainty of the measurement as mentioned in Chap. 1,
which, is stated together with the result of the measurement (the mean) as the full result of a
measurement of a series of measurements for determining a measurand. It is often termed “error of the
mean“.
From Eq. (7) it can be seen that the standard deviation σ of a single measurement remains almost constant
with an increasing number N of measured values in a measurement series, as illustrated in Fig. 5. Addition
of further measured values to the diagram does not affect the deviation of the measured values from the
mean.
Eq. (8), however, demonstrates that the standard deviation σ t of the mean, i.e., the uncertainty of the
measurement decreases with increasing N: The uncertainty decreases by 1/ N . In principle, one can
make it arbitrarily small if only enough measurements are performed. In practice, however, one will repeat
the measurements only until the uncertainty of the measurement meets a given precision requirements. At
this the condition N ≥ 4 must always be met because otherwise no standard deviation (according to Eq. (7)
) can be indicated. This follows from statistical considerations which cannot be discussed in detail here.
In summary, it can be stated:
 The result of a measurement series must always be given in the form t ± σ t .
 The uncertainty of a measurement σ t (the error of the mean) decreases by a factor of 1/ N for an
increasing number of measured values N.
 Unless stated otherwise, the result of a measurement having the form t = (100.6 ± 1.2) s is always
interpreted as follows: Result of the measurement 100.6 s, uncertainty of the measurement (standard
deviation of the mean) 1.2 s.
43
6.4
Absolute and Relative Errors
The quantity σ t represents the absolute error of the measured result, the quantity σ t / t the relative error,
which generally is given as a relative value. During the lab course we will mainly confine ourselves to
stating the absolute errors.
7
Maximum Error of a Single Measurement
It often happens, in the lab course very frequently, that the value of a measurand a is not determined by a
series of measurements but instead only by a single measurement, as e.g. for measurements of lengths. In
that case the result of a single measurement is stated in the laboratory course instead of the mean and the
maximum error ∆a instead of the standard deviation of the mean. This is the biggest possible error occurring
in the single measurement of the quantity. It has to be assessed according to reasonable projections. For
example, when the length of a distance is measured with a ruler, then the reading precision of the ruler will
be taken as the maximum error. E.g. using millimetre measuring tape, the value 0.5 mm is used, whereas
for a vernier caliper a value of 0.1 mm or 0.05 mm is correct, and a value of 0.01 mm is used for a
micrometer gauge.
8
Accuracy
The accuracy of a measured result for the measurand a, i.e. the number of significant digits, is limited by
the uncertainty of the measurement, i.e. the standard deviation σ a of the mean or the maximum error ∆a
of a single measurement, respectively.
σ a and ∆a are to be rounded to maximally two significant digits in this laboratory course! 9
The mean, or, the single measured value resp. is then to be rounded so that its last significant digit has the
same order of magnitude as the last significant digit of σ a or , respectively, of ∆a.
Some examples: An accuracy feigned by calculations, or the number of digits of an electronic clock of the
form: t = 90.4671 s is simply incorrect, if the maximum error of the time measurement is 1.1 s for example.
In this case it should read (rounded): t = (90.5 ± 1.1) s. It is also incorrect to state R = (83.62 ± 2.624) Ω; it
should read R = (83.6 ± 2.6) Ω because of the limitation to two significant digits for the uncertainty of the
measurement.
The significance of a digit is independent of its order of magnitude (position with respect to the decimal
point). Thus, all of the following numbers contain two significant digits:
18
1.8
0.18
0.018
0.0018
etc.
This becomes quite obvious when, as recommended, using powers of ten, i.e., writing the above numbers
as follows:
1.8×101
1.8×100
1.8×10-1
1.8×10-2
1.8×10-3.
In rounding numbers, the question arises in which direction the digit 5 should be rounded. For example,
take the number 4.135, to be rounded to two digits after the decimal point. It would be possible to round it
to 3.14 to 3.13. The rule to apply is to round in such a way, that the last digit of the resulting number is
even. In the example, 4.135 would be rounded upwards to give 4.14. The number 4.125 would in contrast
be rounded downwards to 4.12. The reasoning behind this rule is the fact, that a division by 2 results in
both numbers (rounded and non-rounded) having the same rounded result. For the examples above, we
have:
9
This means that the uncertainty can be stated with an accuracy of approx. 1 %. A better accuracy cannot be achieved with the
equipment in the lab!
44
9
4.135 : 2 = 2.0675 ≈ 2.07
and likewise
4.14 : 2 = 2.07
4.125 : 2 = 2.0625 ≈ 2.06
and likewise
4.12 : 2 = 2.06
Error Propagation, Combined Measurands
It happens frequently that the measured quantity itself (direct measured result) is of no interest, but a
quantity calculated from it (indirect measured result, cf. Chap. 2). Again, let us take as an example the
acceleration of gravity g (Eq. (1) which depends on the measured quantities s and t:
g=
2s
t2
Further examples are the density ρ of a physical body which is a function of the measurands mass m and
volume V:
(9)
ρ=
m
V
or the capacity C of a plate capacitor in vacuum which depends on the measurands surface A of the plates
and the distance d between them. With the electrical field constantε0 we have:
(10)
C = ε0
A
d
All these examples clearly show that the error of the quantity of interest has to be calculated from different
measured quantities. The following chapters describe how this is done.
9.1
The Most Probable Error of a Combined Measured Quantity
In case the result of a measurement for a measurand y is calculated from the results of measurement for
several Gaussian distributed measurands, for which means and standard deviations have been gained from
measurement series, the most probable error for y will be determined by the Gaussian error propagation
law, which is defined in the following.
Let us assume that the quantity of interest y depends on the measurands a, b, c, etc.:
(11)
y = f (a , b, c,...)
We assume that the measured values for the measurands a, b, c,... are Gaussian distributed and do not
influence each other, i.e., they are independent from each other in a statistical sense. We further assume
that the mean values a , b , c , ... and the standard deviations of the means σ a , σ b , σ c , ... are known. Then
the optimum yB 10 for the measurand of interest y is the value obtained by calculating y from the means
a , b , c , ... :
(12)
y B = f ( a , b , c , ...)
This is plausible. The standard deviation σ y of yB is given by the Gaussian error propagation law (illusB
trated in Chap. 9.2) which reads:
2
(13)
10
σ yB =
2
2
 ∂ y 

 ∂ y 

 ∂ y 

σ a  + 
σ b  + 
σ c  + ... :=




 ∂ a  B 
 ∂ b  B 
 ∂ c  B 
The index B stands for best value.
∆ya2 + ∆yb2 + ∆yc2 + ...
45
The expressions ∂y/∂a, ∂y/∂b, etc. in Eq. (13) are the ”partial derivatives“ of y with respect to the quantities
a, b, c,... They state how y would change, if one changes only a, only b, or only c etc. keeping the other
quantities constant. (Mathematically: One forms the derivative of y with respect to each of the quantities a,
b, c,... and considers the other quantities to be constant). The index B in the partial derivatives states that
the numerical values of the partial derivatives are to be calculated at the best values (means) a , b , c , ... of
the measurands a, b, c,….
As an example for the calculation of partial derivatives we take again Eq. (1) for the acceleration of gravity
g, which depends on the quantities s and t. The partial derivative of g by s is:
∂g 2
=
∂s t 2
and the partial derivative of g by t is:
∂g
4s
= − 3
∂t
t
9.2
Illustration of the Error Propagation Law
To illustrate the error propagation law we examine once more the acceleration of gravity g given by Eq. (1)
. We therefore have a function, which depends on the two variables s and t. Eq. (11) then reads with y := g,
a := s and b := t:
g f=
(14)=
( s, t )
2s
t2
In Fig. 6, g is shown as a function of s and t in a 3D-plot, in which the linear dependence of the acceleration
of gravity g of s and the reciprocal-square dependence of t is made clear.
Fig. 6:
Illustration of the error propagation law.
Looking at Fig. 6 we examine the different terms in Eq. (13) more closely, and - as an example - pick the
second one: The quantity to be determined, y (g in this case), depends on the measurand b (t in this case)
among others. When b changes, y will change as well. The partial derivative ∂y/∂b states how big this
change is, i.e., how steep the slope of the function y = f(a, b, c,...) as a function of b is, if one assumes the
remaining quantities a, c,.. to be constant. In the considered example we have:
(15)
∂y ∂g
4s
:=
= − 3
∂b ∂t
t
46
Since this slope is not equally steep everywhere (in the example it changes with t-3, see Eq.(15)), it is useful
to calculate it at the position a , b , c , ... (here s , t ) which is also of relevance for calculating the best
value yB, (here gB). Therefore the index B stands in Eq. (13): (∂y/∂b)B.
Now one needs to know for the term picked out how big the change of yB is, which is caused by the error
σ b . From basic differential calculus it is known that this change is given by the differential:
(16)
∂ y
∆yb :=

 σb
 ∂ b B
here:
∂ g 
4s
∆gt =
− 3 σt

 σt =
t
 ∂ t B
In the same way we can determine the errors
∂ y
 σa
 ∂ a B
(17) =
∆ya : 
(18)
∂ y
∆yc :=

 σc
 ∂ c B
∂ g 
2
=
∆g s  =
σs
here:
 σs
t2
 ∂ s B
(here without importance)
etc., which all add to the total error, i.e. to the standard deviation σ y of yB. Thus it is obvious that they
B
have to be added in order to calculate σ y . Since the single errors according to Eqs. (16) and (18) may be
B
positive or negative, they may partly or entirely neutralize each other and may therefore suggest overall too
small of an error. To avoid this it is useful to square the single errors (which makes them all positive) and
to take the square root of the sum of squares afterwards. Due to this geometrical (quadratic) addition of
single errors, the total error will be smaller than the sum of the single errors. This takes into consideration
that the single errors of the independent quantities a, b, c etc. may not be equally represented in the final
result but may at least partly compensate one another. Hence on speaks of the most probable error.
9.3
Maximum Error of a Combined Measured Quantity
Let us now consider the case (as an example), that the quantities a, b, c etc. either contain no random errors
or their errors have partially not been obtained from measurement series. The latter case occurs frequently
in practice (even in the lab course), namely when at least some of the results of measurement for the
measurands a, b, c etc. have been obtained from single measurements for which only the respective
maximum errors ∆a, ∆b, ∆c are known.
In such a case the maximum error ∆y is stated instead of the standard deviation according to Eq. (13) for
the combined measurand y . It results from the most unfavourable, i.e. arithmetic (linear) addition of all
single errors and is given by:
(19)
∆yB =
∂ y
∂ y
∂ y
∆a +
∆b +
∆c + ...:= ∆ya + ∆yb + ∆yc + ...
∂aB
∂bB
∂cB
where the maximum errors, or respectively, standard deviations are to be inserted for the quantities ∆a,
∆b, ∆c,...
Except for taking the absolute values of the differentials, Eq. (19) is the total differential of yB.
 If not explicitly required otherwise, the maximum error is always to be stated for combined measured
quantities in the laboratory course.
47
10
A Concrete Example
The gravitational acceleration g can be determined using a “mathematical pendulum“. The mathematical
pendulum which can only be realized approximately in practice, consists of a point-like mass in the ideal
case, which is suspended on a massless thread such that it can swing without externally disturbing influences (especially no friction). For displacements of the pendulum by a small angle α below about 5 ° it
holds that α ≈ sin α ≈ tan α (α in radian) and the following relationship between the period of oscillation
T of the pendulum, the length of the thread l, and the gravitational acceleration g is in good approximation
given by:
(20)
T = 2π
l
g
and
g=
4π 2l
, respectively.
T2
By measuring l and T it is thus possible to determine g. Already prior to the measurement we can state the
possible systematic errors:
- Contrary to theory the mass is not pointlike and the thread is not massless. How this affects the measurement is difficult to state. One tries to approach the mathematical pendulum as far as possible by using
a very thin thread and a mass with little spatial expansion and expects that the remaining errors will be
so small compared to the measurement uncertainties of the measurands l and T, that they can be
neglected.
- The pendulum cannot be suspended so that it is completely frictionless. Hence, sufficient effort needs
to be made towards achieving an optimal suspension so that the error caused by friction is small
compared to the measurement uncertainties.
When preparing the experiment care must be taken, so that the watch for measuring T as well as the ruler
for measuring l are calibrated in order to exclude systematic errors caused by inadequate instruments. Additionally, the length of the thread l must be chosen such that the measurement can be carried out using
pendulum swings below approx. 5°, since Eq. (20) is only valid in good approximation under that condition.
After completing these preparations, the measurement can be performed. It is known that the influence of
random errors on the uncertainty of measurement can be minimized by repeating the measurement as often
as possible. At the same time one recognizes that repeated measurements of the length l are of no use at all.
If the ruler is placed and read carefully, the measured value will not change when the measurement is
repeated several times. Nevertheless, the measured value for l is, of course, also subject to an error: On the
one hand, the ruler only has a certain accuracy even when following calibration, on the other hand, it can
only be placed and read with finite accuracy. The result of the measurement can then be stated as follows:
(21)
l= L ± ∆L ;
=
l
e.g.
( 2,5580 ± 0, 0020 ) m
L being the value read and ∆L its maximum error.
The oscillation period T is determined using a stop watch, the internal inaccuracy of which is assumed to
be negligible. Starting and stopping the watch depends on the reaction time of the user and are thus subject
to random fluctuations. Its influence on the uncertainty of the measured result can be minimized by repeated
measurements. After a total of N measurements yielding the values Ti, the result of the time measurement
is:
(22)
T= T ± σ T ;
T
e.g. =
( 3.210 ± 0.010 ) s
with T being the arithmetic mean of measured values Ti according to Eq. (6) and hence the best value for
T, and σ T being the standard deviation of the mean T according to Eq. (8).
Hence, the best value gB for g is according to eq. (20):
48
(23)
gB =
4π 2 L
;
T2
in the example
=
gB
4π 2 × 2,5580 m
m
=
9,801 2
2
s
( 3, 210 s )
Because L has not been determined from a measurement series, not the standard deviation is calculated
using Eq. (13), but the maximum error ∆gB according to Eq.(19). This results in:
(24)
gB
∆=
∂ g
∂ g
σ
∆L +
∂ l B
∂ TB T
First, the absolute values of the partial derivatives at the positions of the best values B (in this case for the
values L and T ) are calculated:
4π 2
4π 2
∂ g
=
= =
∂ l L,T
T 2 L,T
T2
4π 2
= ...
( 3, 210 s )2
(25)
∂ g
∂ T
L ,T
8π 2l
8π 2 L 8π 2 × 2,5580 m
...
=
− 3
=
=
=
T L,T
T3
( 3, 210 s )3
Substituting L and T , equation (25) yields two numbers which are multiplied with two other numbers
according to Eq. (24) (namely ∆L and σ T ), and must be added to find the value of interest, ∆gB:
∆g B =
4π 2
8π 2 L
∆
+
σT
L
T2
T3
(26)
=
4π 2
( 3, 210 s )
2
0, 0020 m +
8π 2 × 2,5580 m
( 3, 210 s )
3
0, 010 s = 0,069
m
s2
In the statement of the numerical value the rounding to two significant digits must be adhered to.
Summarizing, the full result of the measurement reads:
(27)
g= g B ± ∆g B=
( 9.801 ± 0.069 )
m
s2
Since a value for g is available from the literature in this example (which can be found 11 in the appropriate
tabular works for the local geographical coordinates) that value must be compared with the result of the
measurement. If the value for g in the literature is within the range gB ± ∆gB the experiment is completed
and concluded by the statement that a “good agreement within the frame of the measuring accuracy“ was
achieved. If the value from the literature is not within the range gB ± ∆gB however, there is a relatively high
probability that the measurement has been falsified by a systematic error.
Instead of the absolute error ∆gB of the measured result gB one can also state the relative error εg for gB:
(28)
εg =
∆g B
gB
thus:
11
See for example http://www.ptb.de/cartoweb3/SISproject.php
49
(29)
ε=
g
σ
∆L
+2 T
L
T
From this equation is can be seen that the relative error of T , σ T / T is embodied doubly in the result,
however, the relative error of L, ∆L/L, only simply. If this is to be compensated, the relative error of T
may only be half as big as the relative error of L. This can always be achieved by a sufficient number of
measurements of the oscillation period (cf. Eq. (8)) and should already be considered when planning the
experiment.
11
Appendix
11.1 Linear Regression, Regression Lines
11.1.1 Regression Lines of the Form y = ax + b
In practice it happens quite often that two quantities x and y depend linearly on each other, which means
that they are linked by the equation of a line: y = ax + b. The aim of the measurement is then to find the
quantities a and b. Let us take as an example the time behaviour of a velocity v of a constantly accelerated
motion: v(t) = at + v0 with a: acceleration, t: time, and v0 initial velocity. We measure v(t) (dependent
variable) with certain default values of t (independent variable), to obtain a value for the acceleration a and
the initial velocity v0.
Plotting v(t) against t according to Fig. 7, we expect that the sketched points are on a line with a slope that
represents the value of a and a ν-interception that gives us v0. Trying to plot this line in the diagram of
measured values, we notice that quite a few slopes and values for v at t=0 match our measured values more
or less. Which parameters are the right ones? This question can only be answered in the sense of a
probability statement. We want to give the answer in the following.
We return to our function y = ax + b. As frequently occurs in practice, we have N values of the quantity x,
from which we determine N measured values for the quantity y. The errors of the specified values of x are
negligible; the errors of the measured values of y shall be randomly distributed. We maintain, that the
optimums of A and B for the parameters a and b of the line equation have been found, when the sum of the
squares of the vertical distances between the measured values and the „regression line” determined by A
and B, are minimal:
N
(30)
∑  yi − ( Axi + B )
2
→ Minimum
i =1
Question 3:
- How can this approach be justified?
Fig. 7:
Which is the best regression line through the red measured values?
50
By means of differential calculus one may quite easily determine a solution for the requirements described
in Eq. (30). It is found for A and B (summation from 1 to N each):
(31)
(32)
A=
N ( ∑ xi yi ) − ( ∑ xi )( ∑ yi )
B=
( ∑ yi )( ∑ xi2 ) − ( ∑ xi yi )( ∑ xi )
N ( ∑ xi2 ) − ( ∑ xi )2
N ( ∑ xi2 ) − ( ∑ xi )2
Of course, these optimums are also subject to errors, which we are now looking for. The faulty quantities
in Eqs. (31) and (32) are the yi. For the variance of the yi we obtain the optimum (cf. Eq. (7)):
=
σ y
(33)
2
1
2
( Axi + B − yi )
∑
N −2
Division by (N - 2) instead of (N - 1) is explained by the fact that the optimums A and B enter the calculation
of the quantity σ y2 . Applying the error propagation to Eqs. (31) and (32) and inserting Eq. (33) for σy, we
find as optimums for the standard deviations of A and B (D is an auxiliary quantity defined in Eq. (36)):
(34)
σ A = ND
(35)
σ B = D ∑ xi2
with
1 ∑ ( Axi + B − yi )
D=
N − 2 N (∑ xi2 ) − (∑ xi ) 2
2
(36)
In this laboratory course, the software Origin is used for these calculations, as it can produce this data
with a few clicks of the mouse (→ Analysis → Fit → Linear Fit). Do not calculate the parameters of
regression lines „by hand”. That would be much too time-consuming!
11.1.2 Regression Lines of the Gorm y = ax + b with Predefined b
In practice measured values are also connected with each other by a linear function y = ax + b for which
the intercept of the axis is predefined. Let us take OHM’s law U = RI as an example: if we measure the
voltage U as a function of the current I, then the regression line through the measured values is a straight
line through the origin (b = 0) with the slope R. The condition for calculating the optimum A of the slope a
of the regression line in this case reads
N
(37)
∑[
i =1
2
yi − Axi ] → Minimum
analogous to Eq. (30). We obtain for A by means of differential calculus and for σA by means of error
propagation:
(38)
A=
∑x y
∑x
i
2
i
i
51
(39)
σA =
∑( y
i
− Axi )
2
( N − 2 ) ∑ xi2
In order to perform the corresponding calculations with Origin, the box labeled “Fixed intercept at”
must be checked in the window Linear Fit, and the value of b must be entered.
11.1.3 Regression Lines of the Form y = ax + b with Predefined a
The reverse case, in which the slope a of the regression line is predetermined and we are only looking for
the intercept of the axis b occurs occasionally. The condition for calculating the optimum B of b again reads
analogous to Eq. (30):
2
N
(40)
∑  yi − ( axi + B ) → Minimum
i =1
only B being a free parameter for determining the minimum in this case. For B and σB we obtain in this
case:
(41)
B=
∑y
=
σB
(42)
− a ∑ xi
i
N
1
( axi + B − yi )2
∑
N ( N − 2)
In order to perform the corresponding calculations with Origin, the field Fixed Slope in the window
Linear Fit must be checked and the numerical value for a must be entered.
11.2 Linearizations
By means of quite elemental mathematical rearrangement we may linearize non-linear relations between
measured values in order to use linear regression for the calculation of the optimums of the desired values.
For example, the power relationship of the form:
(43)
y = bx a
becomes the linear relationship by a simple logarithm (line equation):
(44)
log
log
hence y =
b + ax
y =
b + a log
x
y
b
x
For logarithmic relationships we have to consider the following: the logarithm of a physical quantity y,
which is given by the product of numerical value and unit, cannot be calculated directly, because the
logarithm of a unit does not make sense. This is why we have to divide these values by their unit to make
them dimensionless. After that we may do rewritings according to the one from Eq. (43) to Eq. (44). An
example: The ohmic resistance R becomes r = R/Ω, the voltage U becomes u = U/V, the current I becomes
i = I/A and so the OHM's law R = U/I becomes the modified form r = u/i, which reads log r = log u – log i
in the logarithmic form.
Plotting y against x (Eq. (44)) on a double-logarithmic scale (i.e. log y over log x), we obtain a line with the
intercept log b and the slope a from the best fit line through the measuring points. The optimums for a and
log b are found by means of a linear regression, to which we have to apply to Eq. (44).
Using the logarithm also converts an exponential relationship of the form
(45)
y = be ax
52
into a linear relationship:
ln y =
ln b + ax ln e =
ln b + ax
(46)
Plotting y against x on a half-logarithmic scale, we also obtain from the best regression a line with intercept
ln b and slope a, the optimums of which are found by applying the linear regression to Eq. (46).
If logarithmic papers are used, we have to consider that this paper is always meant for the use of decimal
logarithm (log, basis 10). However Eq. (46) deals with natural logarithm (ln, base e). Hence, when values
are to be taken graphically from a diagram on logarithmic paper or calculated values are to be entered, they
have to be converted appropriately. (Remember: log x = ln x/ln 10; ln x = log x/log e).
The software Origin is used in the laboratory course for the calculating parameters of regression lines
in logarithmic diagrams. In order to do this, it is necessary to check the field Apparent Fit 12 in the window
Linear Fit.
Question 4:
I ( x ) I 0 exp ( − µ x ) describes the attenuation of the intensity I of a
- The exponential attenuation law=
radiation passing through a layer of matter of thickness x. I0 is the initial intensity of the radiation at the
location x = 0 and µ a material dependent coefficient ([µ] = 1/m). Draw I(x) in linear and semilog
representation (abscissa x linear each). How do we obtain the attenuation coefficient µ from the semilog
diagram ?
11.3 Correlation
Sometimes, although rather rarely in an introductory laboratory course, it has to be examined whether a
presumed linear connection exists between two quantities x and y, i.e., whether the two values are correlated. The diagram of the measured values does not always show whether the plotted values are well fit
to a line or not. In any case the question arises of how “well“ is “well enough“, in order to maintain the
hypothesis that x and y are correlated? The quantitative answer of this question is yielded by the calculation
of the correlation coefficient. It is given by:
(47)
r=
∑ ( xi − x )( yi − y )
∑ ( xi − x )2 ∑ ( yi − y )2
x and y being the arithmetic means of the measured values x and y. The correlation coefficient can only
range between -1 and +1. For judgement of the question of whether two values are correlated, the magnitude
of r is decisive. For |r| = 1 the values are correlated perfectly, for |r| = 0 they are not correlated. For all
values between 0 and 1 probability statements can be made which additionally depend on the amount of N,
i.e. the number of measurements. For N = 10 and |r| ≥ 0.8 e.g. the probability P that the values are not
correlated is P = 0.5 %. Table 1 (Chap. 11.5) in the annex shows the probabilities for further combinations
of N and |r|.
11.4 Errors of Weighted Means
We assume that a measurand h is measured in M measurements (No. i = 1,...,M) under varying conditions.
The result of the single measurements yielded the results hi and the uncertainties σi.
Our aim is to calculate a final result for the quantity of interest h from the M measured values hi . In the
easiest case this would be the arithmetic mean of the hi, however, this would not consider the fact that the
hi may exhibit quite different uncertainties σi because, e.g., the optimum measurement accuracy was not
the same in all measurement series.
12
The fit is called apparent since the data in the Origin worksheet remain in their original, linear form. The data only appear
logarithmic in the diagram on which the fit is based, provided the scaling of the appropriate axes is set to the Type “log10“,
“log2“ or “ln“.
53
In such cases a weighted mean hg. is calculated instead of the arithmetic mean of the hi. If gi are the weights
to be considered for the single values hi when calculating hg , we obtain for summation from 1 to M:
(48)
hg =
∑ hi gi
∑ gi
Generally the reciprocal values of the variances are chosen as weights:
(49)
gi =
1
σ i2
Applying the error propagation law to Eq. (48) we obtain for the uncertainty σg of the weighted mean when
summing up from 1 to M:
2
σg
=
(50)
 ∂ hg 
=
∑ ∂ h σi 
i



1 
 ∑ 2 
 σi 
−
1
2
Question 5:
- How do we obtain that result? What σg is found for the special case gi = const. = 1?
54
11.5
Tables
Table 1:
Percentage probabilities that two quantities measured N times and having a correlation coefficient of
|r| ≥ |rb| are uncorrelated (after /1/).
|rb|→
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
N↓
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
25
30
35
40
45
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
94
90
87
85
83
81
80
78
77
76
75
73
72
71
70
69
68
67
63
60
57
54
51
87
80
75
70
67
63
61
58
56
53
51
49
47
46
44
43
41
40
34
29
25
22
19
81
70
62
56
51
47
43
40
37
34
32
30
28
26
24
23
21
20
15
11
8.0
6.0
4.5
74
60
50
43
37
33
29
25
22
20
18
16
14
12
11
10
9.0
8.1
4.8
2.9
1.7
1.1
0.6
67
50
39
31
25
21
17
14
12
9.8
8.2
6.9
5.8
4.9
4.1
3.5
2.9
2.5
1.1
0.5
0.2
0.1
59
40
28
21
15
12
8.8
6.7
5.1
3.9
3.0
2.3
1.8
1.4
1.1
0.8
0.7
0.5
0.2
0.1
51
30
19
12
8.0
5.3
3.6
2.4
1.6
1.1
0.8
0.5
0.4
0.3
0.2
0.1
0.1
0.1
41
20
10
5.6
3.1
1.7
1.0
0.5
0.3
0.2
0.1
0.1
29
10
3.7
1.4
0.6
0.2
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
|rb|→
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
N↓
50
60
70
80
90
100
100
100
100
100
100
100
73
70
68
66
64
62
49
45
41
38
35
32
30
25
22
18
16
14
16
13
9.7
7.5
5.9
4.6
8.0
5.4
3.7
2.5
1.7
1.2
3.4
2.0
1.2
0.7
0.4
0.2
1.3
0.6
0.3
0.1
0.1
0.4
0.2
0.1
0.1
55
Table 2:
Values of the integrals P(a) over the Gaussian (“error-function”) as a function of the parameter a for any
values of the mean t and standard deviation σ (from /1/; note factor 100 compared to Eq. (5) and following
equations):
P(a ) =
100
σ 2π
t + aσ
∫
−
e
( t − t )2
2σ 2
t − aσ
Exemplarily marked: P(a=1.00) = 68.27, P(a = 2.00) = 95.45, P(a = 3.00) = 99.73
a
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
2.0
2.1
2.2
2.3
2.4
2.5
2.6
2.7
2.8
2.9
3.0
3.5
4.0
4.5
5.0
0.00
0.00
7.97
15.85
23.58
31.08
38.29
45.15
51.61
57.63
63.19
68.27
72.87
76.99
80.64
83.85
86.64
89.04
91.09
92.81
94.26
95.45
96.43
97.22
97.86
98.36
98.76
99.07
99.31
99.49
99.63
99.73
99.95
99.994
99.9993
99.99994
0.01
0.80
8.76
16.63
24.34
31.82
38.99
45.81
52.23
58.21
63.72
68.75
73.30
77.37
80.98
84.15
86.90
89.26
91.27
91.97
94.39
95.56
96.51
97.29
97.91
98.40
98.79
99.09
99.33
99.50
99.64
0.02
1.60
9.55
17.41
25.10
32.55
39.69
46.47
52.85
58.78
64.24
69.23
73.73
77.75
81.32
84.44
87.15
89.48
91.46
93.12
94.51
95.66
96.60
97.36
97.97
98.45
98.83
99.12
99.35
99.52
99.65
0.03
2.39
10.34
18.19
25.86
33.28
40.39
47.13
53.46
39.35
64.76
69.70
74.15
78.13
81.65
84.73
87.40
89.69
91.64
93.28
94.64
95.76
96.68
97.43
98.02
98.49
98.86
99.15
99.37
99.53
99.66
0.04
3.19
11.13
18.97
26.61
34.01
41.08
47.78
54.07
59.91
65.28
70.17
74.57
78.50
81.98
85.01
87.64
89.90
91.81
93.42
94.76
95.86
96.76
97.49
98.07
98.53
98.89
99.17
99.39
99.55
99.67
0.05
3.99
11.92
19.74
27.37
34.73
41.77
48.43
54.67
60.47
65.79
70.63
74.99
78.87
82.30
85.29
87.89
90.11
91.99
93.57
94.88
95.96
96.84
97.56
98.12
98.57
98.92
99.20
99.40
99.56
99.68
0.06
4.78
12.71
20.51
28.12
35.45
42.45
49.07
55.27
61.02
66.29
71.09
75.40
79.23
82.62
85.57
88.12
90.31
92.16
93.71
95.00
96.06
96.92
97.62
98.17
98.61
98.95
99.22
99.42
99.58
99.69
0.07
5.58
13.50
21.28
28.86
36.16
43.13
49.71
55.87
61.57
66.80
71.54
75.80
79.59
82.93
85.84
88.36
90.51
92.33
93.85
95.12
96.15
97.00
97.68
98.22
98.65
98.98
99.24
99.44
99.59
99.70
0.08
6.38
14.28
22.05
29.61
36.88
43.81
50.35
56.46
62.11
67.29
71.99
76.20
79.95
83.24
86.11
88.59
90.70
92.49
93.99
95.23
96.25
97.07
97.74
98.27
98.69
99.01
99.26
99.46
99.60
99.71
0.09
7.17
15.07
22.82
30.35
37.59
44.48
50.98
57.05
62.65
67.78
72.43
76.60
80.29
83.55
86.38
88.82
90.90
92.65
94.12
95.34
96.34
97.15
97.80
98.32
98.72
99.04
99.29
99.47
99.61
99.72
Was this manual useful for you? yes no
Thank you for your participation!

* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project

Download PDF

advertising