## 6. ERROR CALCULATIONS USING CALCULUS
The material of this chapter is intended for the student who has familiarity with calculus concepts and certain other mathematical techniques. In particular, we will assume familiarity with: (1) Functions of several variables.
At this mathematical level our presentation can be briefer. We can dispense with the tedious explanations and elaborations of previous chapters.
If a result R = R(x,y,z) is calculated from a number of data quantities, x, y and z, then the relation:
holds. This is one of the "chain rules" of calculus. This equation has as many terms as there are variables. Then,
Strictly this is no longer an equality, but an approximation to DR, since the higher order terms in the Taylor expansion have been
neglected. So long as the errors are of the order of a few percent or less, this will not matter.
This equation is now an
Finally, divide equation (6.2) by R:
The factors of the form Δx/x, Δy/y, etc are The Notice the character of the standard form error equation. It has one term for each error
source, and that error value appears only in that one term. The error due to a variable, say x,
is Δx/x, and the This equation clearly shows which error sources are predominant, and which are negligible. This can aid in experiment design, to help the experimenter choose measuring instruments and values of the measured quantities to minimize the overall error in the result. The determinate error equation may be developed even in the early planning stages of the
experiment, The coeficients in each term may have + or - signs, and so may the errors themselves. The standard form error equations also allow one to perform "after-the-fact" correction for the effect of a consistent measurement error (as might happen with a miscalibrated measuring device).
In many error calculations, especially those involving powers, products or quotients, it is convenient to take the logarithm of the expression before taking the differentials.
(3.7) The phase velocity of sound in a string is given in terms of the tension, T, and mass per unit length, m, by - U = (T/M)
^{1/2}(3.8) The index of refraction of a prism is given by: - n = sin[(A+D)/2]/sin[A/2]
where A is the prism angle and D is the angle of deviation of a light ray passing through the prism. Find an expression for the absolute error in n. (3.9) The focal length, f, of a lens if given by:
where p and q are the object and image distances. Write an expression for the fractional error in f. When is this error largest? When is it least?
The use of the chain rule described in section 6.2 correctly preserves relative signs of all quantities, including the signs of the errors. It is therefore appropriate for determinate (signed) errors. Indeterminate errors have indeterminate sign, and their signs are as likely to be positive as negative. The equations resulting from the chain rule must be modified to deal with this situation:
The "worst case" is rather unlikely, especially if many data quantities enter into the calculations. The variations in independently measured quantities have a tendency to offset each other, and the best estimate of error in the result is smaller than the "worst-case" limits of error. Statistical theory provides ways to account for this tendency of "random" data. These methods build upon the "least squares" principle and are strictly applicable to cases where the errors have a nearly-Gaussian distribution. Legendre's principle of least squares asserts that the curve of "best fit" to scattered data is the curve drawn so that the sum of the squares of the data points' deviations from the curve is smallest. See SEc. 8.2 (3). In such cases, the appropriate error measure is the
This
The error measures, Δx/x, etc. are now interpreted as standard deviations, s, therefore the error equation for standard deviations is:
This method of combining the error terms is called "summing in quadrature."
(6.6) What is the fractional error in A (6.7) What is the fractional error in A (6.8) What is the fractional error in 3 (6.9) Derive an expression for the fractional and absolute error in an average of n measurements of a quantity Q when each measurement of Q has a
When the calculated result depends on a number of independently measured quantities, with a number of independent trials for each measurement, the propagation rules of section 6.4 are appropriate. Often some errors dominate others. Consider the multiplication of two quantities, one having an error of 10%, the other having an error of 1%. The error in the product of these two quantities is then: √(10 If two errors are a factor of 10 or more different in size, and combine by
quadrature, the smaller error has negligible effect on the error in the result. In such instances
it is a waste of time to carry out In such cases the experimenter should consider whether experiment redesign, or a different method, or better procedure, might improve the results. Especially if the error in one quantity dominates all of the others, steps should be taken to improve the measurement of that quantity. Conversely, it is usually a waste of time to try to improve measurements of quantities whose errors are already negligible compared to others.
We said that the process of averaging should reduce the size of the error of the mean. That is, the more data you average, the better is the mean. We are now in a position to demonstrate under what conditions that is true.
We are using the word "average" as a verb to describe a
Proof: The mean of n values of x is: The average deviation of the mean is: The average deviation of the mean is obtained from the propagation rule appropriate to average deviations: THEOREM 2: The error in the mean is reduced by the factor
1/√n .
The error estimate is obtained by
taking the square root of the sum of the squares of the deviations.
Proof: The mean of n values of x is: Let the error estimate be the standard deviation. Such errors propagate by equation 6.5: Clearly any constant factor placed before all of the standard deviations "goes along for the ride" in this derivation. Therefore the result is valid for any error measure which is proportional to the standard deviation. © 1996, 2004 by Donald E. Simanek. |