Data analysis for theoretical vs experimental results

In summary, the conversation discusses the different types of data analysis that can be performed on a set of theoretical and experimental results. The speaker suggests using measures such as the average root mean square deviation and visually comparing the data through a graph. They also recommend including error bars to assess the agreement between the theoretical and experimental data. The purpose of the analysis is to accurately understand the relationship between the two data sets.
  • #1
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Hello,

My question is what types of data analysis can I perform on a set of theoretical and experimental results? For example, I have v(x) = cos(x) and I plot my observed data to v(x).

Thanks!
 
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  • #2
It really depends upon what you need to know accurately . Typically one measure would be the average root mean sguare deviation of the predicted values cos(x) from the measured values v(x).
The first best thing to do is look at graph displaying both...preferably displaying expected experimental precision somehow. Then there are many paths to follow but your eyeball is a great tool and this is always my first step.
 
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  • #3
Thank you for your response. My theoretical and measured data are very close. The lab instruction just asked for analysis which is kinda vague. :D
 
  • #4
If you have experimental data and the theoretical formula, the best way to present it is draw the theoretical calculated data as a solid line and plot experimental data as points. It is good to include error bars on the experimental data. If the theoretical curve goes through the error bars, then there is an agreement.
 

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