I Data analysis for theoretical vs experimental results

AI Thread Summary
Data analysis on theoretical versus experimental results can involve calculating the average root mean square deviation between predicted and measured values. A graphical representation is essential, displaying both theoretical curves and experimental data points, ideally with error bars to indicate precision. If the theoretical curve intersects the error bars of the experimental data, it suggests agreement between the two sets. The initial visual comparison is crucial for assessing the closeness of the theoretical and measured data. Overall, clear visualization and statistical measures are key to effective data analysis in this context.
TheBlueDot
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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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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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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
 
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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