Calculating Error in Algebraic Approximation: \Delta f(x,y)

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Discussion Overview

The discussion revolves around the calculation of error in algebraic approximations, specifically focusing on the expression for \(\Delta f(x,y)\) and its application in a biological lab context involving genetics and probability distribution. Participants explore the relationship between different error formulas and their implications in statistical analysis.

Discussion Character

  • Technical explanation
  • Conceptual clarification
  • Debate/contested
  • Homework-related

Main Points Raised

  • One participant presents an initial formula for error, \(\Delta f(x,y) = \sqrt{(\frac{df}{dx})^2+(\frac{df}{dy})^2}\), and questions its relation to another expression involving observed and expected values in a biological experiment.
  • Another participant corrects the initial formula, stating that the correct form is \(\Delta f(x,y) = \sqrt{\left(\frac{\partial f}{\partial x}\Delta x\right)^2+\left(\frac{\partial f}{\partial y}\Delta y\right)^2}\), asserting that this represents the standard deviation of \(f\) under certain conditions.
  • A participant seeks clarification on whether the corrected formula refers to the standard deviation of \(f\) or the chi-square formula presented earlier.
  • A later reply distinguishes the chi-square formula as a method for testing goodness of fit, indicating that it is not the same as the standard deviation formula discussed.

Areas of Agreement / Disagreement

Participants express differing views on the relationship between the formulas discussed, particularly regarding the interpretation of the error formula and the chi-square test. No consensus is reached on whether the formulas are linked or distinct.

Contextual Notes

Participants note that the error formula's validity may depend on assumptions about the independence of variables and the definitions of \(\Delta x\) and \(\Delta y\). The discussion also highlights potential confusion regarding the application of statistical tests in experimental contexts.

Who May Find This Useful

This discussion may be useful for students and researchers in biology, statistics, and related fields who are exploring error analysis, statistical testing, and the application of algebraic approximations in experimental data analysis.

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For a function f(x,y): The error is:

[tex]\Delta f(x,y) = \sqrt{(\frac{df}{dx})^2+(\frac{df}{dy})^2}[/tex]

Is this a form of the approximation in algebraic error determination:

[tex]\Delta f(x,y) = \sqrt{f(x+\Delta x,y) + f(x,y+\Delta y)}[/tex]?

Now I was trying to do this in my bio lab for genetics and probability distribution. We had 27 samples, predicted 75% (20.25) to be purple and 25% (6.75) to be white. We observed that 23 were purple and 4 were white, so to calculate the deviation I used the above function and got:

[tex]\frac{df}{dx} = \frac{23-20.25}{20.25}, \frac{df}{dy} = \frac{4-6.75}{6.75}[/tex]

*For some reason latex doesn't want to show it, but I plugged in my numbers into the first equation above.

So the observed sample was 18.44% off?


They gave us an equation to find the deviation of a population from the expected value which is similar to the one above:

[tex]\chi^2 = {\frac{(Obs_p - Exp_p)}{Exp_p}^2+\frac{(Obs_w - Exp_w)}{Exp_w}^2}[/tex]

[tex]\chi^2 = {\frac{(2.75)}{20.25}^2+\frac{(2.75)}{6.75}^2} = 1.494[/tex]

It claims that "If the value for chi squared is less than or equal to 3.841, then your sample is within the expected range."

Is this a standard deviation?

This is pretty similar to the first equation, except we're squaring the numerator instead of the whole fraction. Did I get my error equation wrong, or are these truly diferent and unlinked?
 
Last edited:
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[tex]\Delta f(x,y) = \sqrt{\left(\frac{\partial f}{\partial x}\right)^2+\left(\frac{\partial f}{\partial y}\right)^2}[/tex]

isn't quite right. It's

[tex]\Delta f(x,y) = \sqrt{\left(\frac{\partial f}{\partial x}\Delta x\right)^2+\left(\frac{\partial f}{\partial y}\Delta y\right)^2}[/tex]

this error formula is indeed just the standard deviation of [itex]f[/itex], assuming [itex]\Delta x[/itex] and [itex]\Delta y[/itex] are standard deviations of the respective variables, and that [itex]x[/itex] and [itex]y[/itex] are independent.
 
Last edited:
Your corrected formula is the standard deviation of f? Or are you talking about my Chi formula? Are they the same?
 
The equation I posted gives the standard deviation of [itex]f[/itex]. Your chi formula is an example of the chi square test for goodness of fit of experimental data to a certainly distribution (the one that the expected values come from). The best online resource that I found to explain it (and it's not very good...) is here: http://www.stat.yale.edu/Courses/1997-98/101/chigf.htm
 

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