Correlation test for model outputs

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SUMMARY

The discussion focuses on comparing a daily water demand prediction model built in Excel with a more accurate 5-minute timestep model. The user, Rob, seeks guidance on the appropriate statistical approach for this comparison. The consensus suggests utilizing Excel's PEARSON or CORRELATION functions to assess linear correlation between the two models. Additionally, a linear regression model in the form of y = mx + c may be employed for a more detailed analysis.

PREREQUISITES
  • Understanding of Excel statistical functions, specifically PEARSON and CORRELATION.
  • Knowledge of linear regression modeling and its components (y = mx + c).
  • Familiarity with data aggregation techniques for time series analysis.
  • Basic concepts of correlation and its applications in statistical analysis.
NEXT STEPS
  • Research the implementation of Excel's PEARSON function for correlation analysis.
  • Learn about linear regression modeling and how to interpret its results.
  • Explore data aggregation methods for time series data in Excel.
  • Investigate advanced statistical tools for correlation analysis beyond Excel.
USEFUL FOR

Data analysts, statisticians, and anyone involved in predictive modeling and statistical analysis of time series data.

Richard_R
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Hello all,

I am currently building a model in Excel for predicting domestic water demand on a daily basis. The daily model will be compared to a more accurate model which runs on timesteps of 5 minutes (the latter is data intensive however which is why we are building a daily model as it requires less data).

Does anyone know what the correct correlation/statistical approach is to compare the daily model results with the 5 minute model? I have aggregated the results from the 5 minute model to a daily timestep so results can be directly compared, i.e.

http://sudsolutions.co.uk/misc/model_results.PNG

Excel has CORRELATION and PEARSON statistical functions so was wondering if I need to use one of these. The results aren't in the form of y=mx+c so I don't think I want an r^2 "goodness of fit" test (or do I?).

Thanks in advance for any help.

Regards
Rob
 
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PEARSON or CORRELATION seem to be the way to go for your data (from excel help I think they do the same thing?)

Correlation can broadly refer to either linear or nonlinear correlation. I suppose you are interested in the linear case, and thus can use a linear regression model in the form of y = mx + c to analyze the data.
 

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