Calculate the chi-squared probability for the fit

AI Thread Summary
The discussion revolves around calculating the chi-squared probability for a linear fit of displacement data from a piezoelectric drive experiment. Participants are debating whether to use the linear equation derived from Excel, y=474.82x+8.3818, for theoretical values in the chi-squared calculation or to assume that the relationship should pass through the origin. There is uncertainty regarding the treatment of the intercept in the context of the experiment's expectations. The importance of accurately representing the data and the implications of the chosen approach on the chi-squared results are emphasized. Ultimately, clarity on how to handle the linearity assumption is crucial for the analysis.
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Homework Statement



an experiment is performed to test whether the displacement D produced on a piezoelectric drive behaved linearly with the potential voltage V applied across it. The following data are obtained:

V (volts) D (um)
0.1 50
0.2 101
0.3 151
0.4 198
0.5 249
0.6 298
0.7 346
0.8 393
0.9 438
1 181
1.1 521

the uncertainty on the voltage setting can be considered negligible. While the displacement measurements are performed with a linear encoder with an uncertainty of +-5um

calculate the chi-squared probability for the fit

The Attempt at a Solution


when I plotted the graph the equation Excel displayed was:
y=474.82x+8.3818

I have thought about two possibilities:

when calculating chi-squared, the values of V(subscript theory) be taken by using the equation on the least-squared-fitted graph on Excel?

y=474.82x+8.3818

or does one assume that V is supposed to be proportional to D and that the graph is meant to go through the origin?

Should I use the above equation to work out what Dis supposed to be in theory when working out chi-squared? or should I assume it's supposed to go through the origin?
 
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by the way, I had trouble getting the values in the table to look presentable on the screen, but I'm sure you can guess what I meant
 
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