Difference between revisions of "TF ErrorAna PropOfErr"

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<math>\Delta A = \Delta L \frac{\partial A}{\partial L} \bigg |_{L_0,W_0} + \Delta W \frac{\partial A}{\partial W} \bigg |_{L_0,W_0}</math>
 
<math>\Delta A = \Delta L \frac{\partial A}{\partial L} \bigg |_{L_0,W_0} + \Delta W \frac{\partial A}{\partial W} \bigg |_{L_0,W_0}</math>
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[http://wiki.iac.isu.edu/index.php/Forest_Error_Analysis_for_the_Physical_Sciences] [[Forest_Error_Analysis_for_the_Physical_Sciences]]

Revision as of 21:07, 9 January 2010

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]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]A = f(L,W)[/math]


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

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

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


The talylor expansion of a function with two variable is given by

[math]f(x_1, x_2)=f({x_o}_1, {x_o}_2)+(x_1-{x_o}_1) \frac{\partial f}{\partial x_1}\bigg |_{(x_1 = x_{01}, x_2 = x_{02})} +(x_2-{x_o}_2) \frac{\partial f}{\partial x_2}\bigg |_{(x_1 = x_{01}, x_2 = x_{02})}[/math]

or

[math]f(x_1, x_2)-f({x_o}_1, {x_o}_2)=(x_1-{x_o}_1) \frac{\partial f}{\partial x_1}\bigg |_{(x_1 = x_{01}, x_2 = x_{02})} +(x_2-{x_o}_2) \frac{\partial f}{\partial x_2}\bigg |_{(x_1 = x_{01}, x_2 = x_{02})}[/math]

The term

[math]f(x_1, x_2)-f({x_o}_1, {x_o}_2)[/math]

represents a small fluctuation of the function from its average.

If we ignore higher order terms in the Taylor expansion ( this means the fluctuations are small)

and

Based on the Definition of Variance

If you were told that the average is [math]\bar{x}[/math] then you can calculate the

"true" variance of the above sample as

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



\[math]Delta A = A- A_0 =f(L,W)-f(L_o,W_0)[/math] = fluctuation of the Area

and simularly

[math]\Delta L = L-L_0[/math] and [math]\Delta W = W-W_0[/math]


Then

[math]\Delta A = \Delta L \frac{\partial A}{\partial L} \bigg |_{L_0,W_0} + \Delta W \frac{\partial A}{\partial W} \bigg |_{L_0,W_0}[/math]



[1] Forest_Error_Analysis_for_the_Physical_Sciences