[R] Linear regression with tranformed dependant variable

[John C Frain]

Before going to stackexchange you should consider if a square root
transformation is appropriate for the model that you are trying to
estimate. If you do so, you may be able to interpret the coefficients
yourself. If no explanation is obvious you probably should not be using a
square root transformation.

Also you might  google "square root transformation regression" and you will
find several useful links.

John C Frain
3 Aranleigh Park
Rathfarnham
Dublin 14
Ireland
www.tcd.ie/Economics/staff/frainj/home.html
mailto:frainj at tcd.ie
mailto:frainj at gmail.com

On 23 October 2017 at 20:11, Rui Barradas <ruipbarradas at sapo.pt> wrote:

> Hello,
>
> R-Help answers questions on R code, your question is about statistics. You
> should try posting the question to
>
> https://stats.stackexchange.com/
>
> Hope this helps,
>
> Rui Barradas
>
> 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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>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide http://www.R-project.org/posti
>> ng-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>>
>>
> ______________________________________________
> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posti
> ng-guide.html
> and provide commented, minimal, self-contained, reproducible code.

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