TF ErrorAna PropOfErr

A quantity which is calculated using quantities with known uncertainties will have an uncertainty based upon the uncertainty of the quantities used in the calculation.

To determine the uncertainty in a quantity which is a function of other quantities, you can consider the dependence of these quantities in terms of a tayler expansion

Consider a calculation of a Table's Area

[math]\displaystyle{ A= L \times W }[/math]

The mean that the Area (A) is a function of the Length (L) and the Width (W) of the table.

[math]\displaystyle{ A = f(L,W) }[/math]

The Taylor series expansion of a function f(x) about the point a is given as

[math]\displaystyle{ f(x) = f(a) + f^{\prime}(x)|_{x=a} \frac{x}{1!} + f^{\prime \prime}(x)|_{x=a} \frac{x^2}{2!} + ... }[/math]

[math]\displaystyle{ = \sum_{n=0}^{infty} f^{(n)}(x)|_{x=a} \frac{x^n}{n!} }[/math]

For small values of x (x << 1) we can expand the function about 0 such that

[math]\displaystyle{ \sqrt{1+x} = \sqrt{1-0} | \frac{1}{2}(1+x)^{-1/2}|_{x=0} \frac{x^1}{1!}+ \frac{1}{2}\frac{-1}{2}(1+x)^{-3/2}|_{x=0} \frac{x^2}{2!} }[/math]

[math]\displaystyle{ =1 + \frac{x}{2} - \frac{x^2}{4} }[/math]

The talylor expansion of a function with two variables[math]\displaystyle{ (x_1 , x_1) }[/math] about the average of the two variables[math]\displaystyle{ (\bar {x_1} , \bar{x_2} ) }[/math] is given by

[math]\displaystyle{ f(x_1, x_2)=f(\bar {x}_1, \bar{x}_2)+(x_1-\bar {x}_1) \frac{\partial f}{\partial x_1}\bigg |_{(x_1 = \bar {x}_1, x_2 = \bar{x}_2)} +(x_2-\bar{x}_2) \frac{\partial f}{\partial x_2}\bigg |_{(x_1 = \bar {x}_1, x_2 = \bar{x}_2)} }[/math]

or

[math]\displaystyle{ f(x_1, x_2)-f(\bar {x}_1, \bar{x}_2)=(x_1-\bar {x}_1) \frac{\partial f}{\partial x_1}\bigg |_{(x_1 = \bar {x}_1, x_2 = \bar{x}_2)} +(x_2-\bar{x}_2) \frac{\partial f}{\partial x_2}\bigg |_{(x_1 = \bar {x}_1, x_2 = \bar{x}_2)} }[/math]

The term

[math]\displaystyle{ f(x_1, x_2)-f(\bar {x}_1, \bar{x}_2) }[/math]

represents a small fluctuation of the function from its average [math]\displaystyle{ f(\bar {x}_1, \bar{x}_2) }[/math] if we ignore higher order terms in the Taylor expansion ( this means the fluctuations are small).

Based on the Definition of Variance

[math]\displaystyle{ \sigma^2 = \frac{\sum_{i=1}^{i=N} (x_i - \bar{x})^2}{N} }[/math]

We can write the variance of the area

[math]\displaystyle{ \sigma^2_A = \frac{\sum_{i=1}^{i=N} (A_i - \bar{A})^2}{N} }[/math]

[math]\displaystyle{ = \frac{\sum_{i=1}^{i=N} \left [ (L-\bar{L}) \frac{\partial A}{\partial L} \bigg |_{\bar L \bar W} + (W-\bar W) \frac{\partial A}{\partial W} \bigg |_{\bar L \bar WW} \right] ^2}{N} }[/math]

[math]\displaystyle{ = \frac{\sum_{i=1}^{i=N} \left [ (L-\bar{L}) \frac{\partial A}{\partial L} \bigg |_{\bar L \bar W} \right ] ^2}{N} + \frac{\sum_{i=1}^{i=N} \left [ (W-\bar W) \frac{\partial A}{\partial W} \bigg |_{\bar L \bar W} \right] ^2 }{N} }[/math]

[math]\displaystyle{ +2 \frac{\sum_{i=1}^{i=N} \left [ (L-\bar{L}) (W-\bar W) \frac{\partial A}{\partial L} \bigg |_{\bar L \bar W} +\frac{\partial A}{\partial W} \bigg |_{\bar L \bar W} \right]^2}{N} }[/math]

[math]\displaystyle{ = \sigma^2_L \left ( \frac{\partial A}{\partial L} \right )^2 +\sigma^2_W \left ( \frac{\partial A}{\partial W} \right )^2 + \sigma^2_{LW} \frac{\partial A}{\partial L} \frac{\partial A}{\partial W} }[/math]

where [math]\displaystyle{ \sigma^2_{LW} = \frac{\sum_{i=1}^{i=N} \left [ (L-\bar{L}) (W-\bar W) \right ]^2}{N} }[/math] is defined as the Covariance between [math]\displaystyle{ L }[/math] and [math]\displaystyle{ W }[/math].

[1] Forest_Error_Analysis_for_the_Physical_Sciences