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The plotted curve is the experimental value of the SPD curve. Though it doesn't look "spikey" as it should, stacking the image on top seems to explain that the data is infact correct, but that more data points (i.e. colours) are needed. However, is it valid to compare the two like this? The horizontal axis is correct yet the vertical axis isn't proportional to the graph's.

Note: the experiment was performed by detecting the intensity of light through the different coloured filters (cellophane used).

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  • $\begingroup$ It would be helpful to spell out acronyms, e.g. CFL SPD? $\endgroup$ – CDCM Sep 2 '17 at 0:35
  • $\begingroup$ It all depends of how accurately you one predict? $\endgroup$ – Creator Sep 2 '17 at 0:45
  • $\begingroup$ I'm not sure that I agree that the experimental data is in good agreement with the blue model curve shown. Yes, the large green (≈490 nm), green-yellow (≈540 nm), and red (≈610 nm) peaks and the narrow peak in the violet (≈430 nm) fit the curve decently if they are all properly scaled. But that about the smaller peaks in the yellow and the small peak in the red at around 620 nm ? Those are way off from their expected intensities. There's also a very small peak at around 710 nm which is quite a bit off from its expected intensity. ..... $\endgroup$ – Samuel Weir Sep 2 '17 at 1:34
  • $\begingroup$ @SamuelWeir Would a bar chart be a better way to express this? Would the different vertical axis affect anything? $\endgroup$ – paradox124 Sep 2 '17 at 1:37
  • $\begingroup$ ... If all these peaks are supposed to be equally weighted, then the fit doesn't look that good. $\endgroup$ – Samuel Weir Sep 2 '17 at 1:37

Plotting like this is an art. It's about presenting the data in a way that makes it easier to understand the data. As such, you have to account for what story you are trying to tell the reader.

The first question I would have is whether the curve fit is worth showing. You have 5 data points in your experimental data, and apparently the story you are trying to tell is that the experimental data lines up with the theoretical data. However, that's not the story I get. While the datapoints themselves line up well, the curves are almost completely unrelated in every way. The story the curve tells is "these light sources are completely different from each other," and I have to look deeper to realize that the measured points are actually really close, it's only the artificial curve-fit that's far. Ditch the curve fit, and instead only show the data points themselves with no line between them. Switch to a bar chart if it's more comfortable.

Also, if you are presenting theoretical data and real data on a chart, label them very explicitly. You never want a customer of your presentation to mistake one for the other. It actually took me a while to realize what was going on because it was hard to figure out which data was real.

Fix those, and you can show graphs which compare different units. However, you want to make it very clear why you were doing so, and why you didn't convert ones data into the other's units.

  • $\begingroup$ Thankyou. I will change the CFL graph to a bar graph and see how it compares. I have added in the results for the LED and halogen bulb (which is a set of poor data), do you think they should also be in bar graphs? $\endgroup$ – paradox124 Sep 2 '17 at 1:41

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