- #1
JulienB
- 408
- 12
Homework Statement
Hi everybody! Our experiments teacher asked us to perform a reduced chi square test in order to estimate how good a model fits to our measured data. The experiment was the melde's experiment (vibration of a string) and we measured the frequency ##f_n## for ##n=1## to ##9##. The string had a fixed length ##l=0.6##m and we had an unknown tension ##F_0## acting on the string. We therefore had to perform a linear regression in order to find ##c_{transverse}##.
To find the standard deviation, we measured ##f_9## six times, calculated its standard deviation and applied it to all ##f_n## as an estimation. Therefore, ##\sigma = 2.2358##Hz for each measured frequency. We got nevertheless a different overall gaussian uncertainty for each frequency due to the apparatus, but after rounding every uncertainty is equal to ##\Delta f_n = 2##Hz.
The (linear) fit model is of the form ##f(n) = a \cdot n## with ##a = 159.4## (see fit in attached picture). Here is a table with the relevant data, what we measured and what the fit model predicts. Note that in the following calculations I'm using the rounded data, but I imagine it should normally not be rounded in the reduced chi square test. The reason is that I don't have the original data with me right now, but I'll use it instead as soon as I get it. Until then I assume it makes only little difference.
[tex]
\begin{array}{l ccccccccc}
\hline
n & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 \\ \hline
f_{n,measured} & 158 & 316 & 475 & 637 & 793 & 963 & 1111 & 1281 & 1432 \\
f_{n,expected} & 159.4 & 318.8 & 478.2 & 637.6 & 797 & 956.4 & 1115.8 & 1275.2 & 1434.6 \\ \hline
\end{array}
[/tex]
Homework Equations
So here is my first confusion: the equations to calculate chi square are not the same depending on the source! Maybe there's an explanation I don't understand. I find mostly
##\chi^2 = \sum \frac{(\mbox{observed} - \mbox{expected})^2}{\mbox{expected}}##
but sometimes it is
##\chi^2 = \sum \bigg( \frac{\mbox{observed} - \mbox{expected}}{\sigma} \bigg)^2##
and those don't seem equivalent to me, unless I misunderstand what "observed" and "expected" mean... In some sources "expected" refers to what the model predicts, but sometimes it seems to refer to the mean value! I would assume "expected" = mean value when random events are measured, but it is hard to find a confirmation through Google. Anyway the reduced chi square test is then
##\chi_{red}^2 = \frac{\chi^2}{\mbox{DoF}}##
The Attempt at a Solution
Okay I have not much to write in this section. I have tried MANY ways to make this work, but I can't trust any result. If I stick to my first understanding of ##\chi^2##, I get
##\chi^2 = \sum \frac{(f_{n,measured} - f_{n,expected})^2}{f_{n,expected}} = 0.1762##
which doesn't really look like what I expected. As a matter of fact, the reduced chi square test gives a very unsatisfying value of
##\chi_{red}^2 = 0.0220##
where I considered ##\mbox{DoF} = 8## (is that right?). The value indicates I would be overfitting the data, or that the error was overestimated. But how is the error even playing a role here? I find it also hard to believe that it would be overestimated. I must be doing something wrong, but I can't find where.Thanks a lot in advance for your help, I'm looking forward to reading your suggestions.Julien.