Data analysis for theoretical vs experimental results

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SUMMARY

This discussion focuses on data analysis techniques for comparing theoretical results, specifically the function v(x) = cos(x), with experimental data. Key methods include calculating the average root mean square deviation between predicted and measured values, and visually comparing the data through graphs. Theoretical data should be represented as a solid line, while experimental results should be plotted as points with error bars to assess agreement. The presence of error bars that overlap with the theoretical curve indicates a successful correlation between the two data sets.

PREREQUISITES
  • Understanding of root mean square deviation (RMSD)
  • Proficiency in data visualization techniques
  • Familiarity with plotting software (e.g., Python's Matplotlib or R's ggplot2)
  • Knowledge of error analysis and representation
NEXT STEPS
  • Learn how to calculate average root mean square deviation (RMSD) for data sets
  • Explore data visualization techniques using Python's Matplotlib
  • Research error bar representation in experimental data
  • Study methods for comparing theoretical models with experimental results
USEFUL FOR

Researchers, data analysts, and students involved in experimental physics or engineering who need to analyze and present theoretical versus experimental data effectively.

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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