Undergrad Linear regression on data collection error

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Significant differences in linear regression results (R^2 values) between data sets may indicate potential data collection errors. A comparison of two sets with R^2 values of 0.720 and 0.810 against others with values above 0.900 raises questions about their validity. The uncertainty in R^2 can be influenced by dataset size and distribution, suggesting that weak results warrant further investigation. It is crucial to analyze the data for outliers that could skew the regression equations. Evaluating the impact of removing these outliers could provide clearer insights into the regression results.
Travis T
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Hi

I've collected few sets of data and obtained significant different linear regression (R^2) in 2 particular sets of data .
Does that indicates the 2 sets of data is not validated which might due to data collection error?

For example, 20 sets of data contain linear regression of 0.900+ (0.994, 0.983, 0.932...), while the 2 sets of data contain linear regression of 0.720 and 0.810 respectively.
 
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It depends on the uncertainty on R, which depends on the size of the datasets and the distribution of the data.
 
Are the regression equations significantly different or just a smaller R2? If 2 out of 20 are weak results, you should only be suspicious if their estimates are very different. The unusually high R2 might mean that those sets have some outliers. You may want to look at the data and see if some points look unreasonable. If there are outliers pulling the regression equation out of line with the others, I would see what happens if the outliers are thrown out.
 
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