Re: st: variable transformation and centering

[Eduardo Nunez <enunezb@gmail.com>]

Thank you Nick.

Eduardo

On Sun, Apr 3, 2011 at 8:18 PM, Nick Cox <njcoxstata@gmail.com> wrote:
> The marginal distribution of any predictor does not have implications
> as such for regression.
>
> Possibly, transforming skewed predictors may indirectly improve
> matters in so far as assumptions of linearity, additivity and equal
> variances are more nearly satisfied.
>
> However, I suggest that your question answers itself if you focus on
> one detail. From what you say, transformations such as logarithm or
> square root are the most likely candidates. Translating your
> predictors first by subtracting a summary will produce variables with
> positive and negative values which will only be more difficult to
> transform. So, don't centre first. Typically, centring will not be
> necessary after transformation either.
>
> On Sun, Apr 3, 2011 at 10:08 PM, Eduardo Nunez <enunezb@gmail.com> wrote:
>
>> I have to run a regression model with several inflammatory biomarkers
>> as dependent variables. They are continuous and heavily skewed
>> variables.
>> I would like to transform and center them before including in the model.
>> My question is: should I transform the variable first and then center
>> it? Or is preferred to center the variable first and then to transform
>> it?
>> Or any way doesn't make difference?
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