[R] Linear regression with tranformed dependant variable

[Michael Friendly]

Step back a minute:  normality is NOT required for predictors in a 
multiple regression model, though the sqrt(x) transformation may
also make the relationship more nearly linear, and linearity IS
assumed when you fit a simple model such as y ~ x + w + z.
(Normality is only required for the residuals/errors)

To see what's going on, you can make make partial regression /
added-variable plots using car::avplots. The loess smooth will
show you if the relationship is non-linear.

HTH
-Michael

> Em 23-10-2017 18:54, kende jan via R-help escreveu:
>> Dear all, I am trying to fit a multiple linear regression model with a 
>> transformed dependant variable (the normality assumption was not 
>> verified...). I have realised a sqrt(variable) transformation... The 
>> results are great, but I don't know how to interprete the beta 
>> coefficients... Is it possible to do another transformation to get 
>> interpretable beta coefficients to express the variations in the 
>> original untransformed dependant variable ? Thank you very much for 
>> your help!Noémie
>>     [[alternative HTML version deleted]]
>>
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