# Re: st:Transformation for skewed variables with negative values?

 From "woong-tae chung" To Subject Re: st:Transformation for skewed variables with negative values? Date Sun, 15 Aug 2004 11:20:01 -0600

```Thanks for the wonderful comment on my inquiry.
WT

----- Original Message -----
From: "Joseph Coveney" <jcoveney@bigplanet.com>
To: "Statalist" <statalist@hsphsun2.harvard.edu>
Sent: Tuesday, August 15, 2006 10:50 AM
Subject: Re: st:Transformation for skewed variables with negative values?

> Woong Chung wrote:
>
> I need following help. I have panel dataset for estimating a simple linear
> equation.
> The problem is that my all variables have sknewness and big variation(large
> std).
> In particualr, the dependent variable and one of independant variables have
> a negative sknewness, while all other independant variables are shown by
> positive sknewness. My first intension is using a log transformation of all
> variables  but seems not to be a good idea since all variables have negative
> values (around 20%)
> Besides, all variables except one of independant variables are ratio, thus
> that idea would make worse.
>
> I would be so glad if anyone has suggestions to solve this problem
>
> --------------------------------------------------------------------------------
>
> It's not clear that you actually have a problem.
>
> It shouldn't be a problem that your independent variables are skewed or have
> a wide distribution.  There isn't any assumption their distribution, and it
> is considered better to for them to cover more ground.  They're only assumed
> not to comprise a linear combination within machine precision.  (There are
> other assumptions about them, in particular, about their relation to the
> random effects, but that's another matter.)
>
> Fit the model as-is.  Examine the residuals and empirical Bayes predictions.
> If these do not have a reasonably normal-appearing distribution, then
> transform the dependent variable in accordance with shaping-up their
> distributions, and not the dependent variable's distribution per se.
>
> Also, from your description, it seems that your dependent variable is a
> ratio.  Consider sticking its denominator in the model as a predictor and
> using its numerator as the dependent variable.
>
> Joseph Coveney
>
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```