Multiple linear regression + QQplots problem Includes pics

AI Thread Summary
The discussion addresses concerns about the normality of residuals in multiple linear regression, specifically noting that while 95% of the data fits the normal line in a QQ plot, 5% deviates significantly. This non-normality, particularly in the tails, raises questions about the reliability of least squares estimates. Participants suggest exploring robust regression methods, such as those available in the MASS package in R, to mitigate issues caused by non-normality. The original poster acknowledges the advice and expresses intent to try robust regression. The conversation emphasizes the importance of addressing residual normality for valid regression analysis.
emelie_earl
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I want to do multiple linear regression, but one of the requirements is the residuals to be normally distributed, and I can check that with QQplots but then the QQ plot shows it is about 95% of data fit into the normal line, but 5% is way off!

can I still proceed ?*or do I have to find a way to transform the data ?*


5.jpg


5_residuals.jpg
 
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Your plots show serious non-normality in the error structure, with (as you've noted) problems in the tails, and since least squares is incredibly non-robust you're correct to be concerned.
1) Have you noticed any strange behavior in your estimates (coefficients with signs opposite what you might expect)?
2) Have you tried a robust regression? The MASS package in R provides several good options.
 
statdad said:
Your plots show serious non-normality in the error structure, with (as you've noted) problems in the tails, and since least squares is incredibly non-robust you're correct to be concerned.
1) Have you noticed any strange behavior in your estimates (coefficients with signs opposite what you might expect)?
2) Have you tried a robust regression? The MASS package in R provides several good options.


Thank you!
I will try Robust regression.
 
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