Photoelectric effect and Planck's Constant

AI Thread Summary
The discussion revolves around calculating Planck's constant from lab data involving stopping voltages and light wavelengths. The user obtained a value of 3E-15 eV*s, which is lower than the standard value of 4.136 eV*s, and seeks advice on improving accuracy through error analysis in linear regression. Suggestions include using error bars in Excel and potentially excluding outlier data points that significantly deviate from the mean. The conversation also touches on methods for converting uncertainty in wavelength to uncertainty in frequency, highlighting the importance of documenting any data exclusions in the lab report. Overall, the focus is on refining the data analysis process to achieve a more accurate measurement of Planck's constant.
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Homework Statement


I'm trying to make sense of some data taken during a lab in which we were tasked with obtaining a value for Planck's constant by measuring different stopping voltages from varying wavelengths of light. The value I got was 3E-15 eV*s, but I know I can get a better value if I account for the error when calculating linear regression. What I need help figuring out is how to do that.


Homework Equations


V=hv-W


The Attempt at a Solution


http://tinypic.com/r/b7fm1u/5
 
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Didn't know you could attach files! Here's the actual spreadsheet.
 

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There is a lot of variation in the stopping voltage for some of the optical wavelengths - I would attempt to reduce the variance here. This is the cause of the not-so-great r^2=.92.

Your value of 3e-15 eV*s is not too bad for an undergraduate lab; the standard value is 4.136 eV*s.
 
Is there a way for me to give more weight to points with less error, or to account for error bars when calculating regression Excel?
 
For error bars with Excel:
http://office.microsoft.com/en-us/e...remove-error-bars-in-a-chart-HP010342159.aspx

You could exclude the data with the largest std. dev. - but you have to explain _why_ you did this in the lab report. I've never reported scientific or engineering data which has been "weighted" based on the error bars.

Usually if the error is inconsistent between the various cases it means that there are methodological, procedural, or instrumentation sensitivity issues. For example, trial 3 for 445 nm is way off the mean of the other four trials; could this be a transcription error? Was there something else happening with this trial?

Because that one point is inconsistent with the rest of the data (an outlier) document it, remove it, and run the regression without that one point.
 
Thanks for your help. I have one more question for anyone who knows the answer. What's the method I used to find uncertainty in frequency? It is shown in the Excel file, but I can't remember what it's called.
 
From looking at your spreadsheet it looks like you used bandwidth from a spec sheet; or it may have been called "line width" in which case you had to convert it to a frequency.
 
Yes, but do you have any idea what the process I used to change uncertainty in wavelength to uncertainty in frequency was? Something like:
deltax=((x+deltax)-(x-deltax))/2
 
Bandwidth is the range of frequencies in the source - your numbers look like lamps, not lasers.

If given as a linewidth (perhaps from a diffraction grating) then you have the relationship:

frequency = speed of light / wavelength

Then the difference in two frequencies is:

f2 - f1 = c*(1/w2 - 1/w1)
 
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