Highland Statistics Ltd. wrote:
> On Tue, 13 Nov 2007 20:07:33 +0200, Anon. <[EMAIL PROTECTED]> wrote:
> 
>> Sami Ullah wrote:
>>> Hey Ecologers:
>>>
>>> I have a various variables for running multiple linear regression model
>>> using GLM. Some of my predictor variables are non-normally distributed.
>>> Using multiple linear regression, I use proc-univariate to check if the
>>> residuals in the regression model met the normality criteria, which the
>>> model did.
>>>
>>> Now I am wondering if it is advisable if I can keep the skewed predictor
>>> variables in the model or have to go for non-parametric analysis?
>>>
>>>
>> The distribution of the predictor variables is irrelevant, so you can
>> happily keep them in.  Well, the distribution is almost irrelevant.  You
>> can get problems if they are co-linear (i.e. highly correlated), or if
>> you have outliers (which can have a large influence on the fit).
> 
> Agree. One extra thing....I would argue that normality of explanatory 
> variables (predictors) is actually bad. It means that most observations 
> have the same (or similar) value for that explanatory variable, which may 
> (!) make it more difficult to find a significant effect. Bad experimental 
> design. Perhaps a histogram shaped like the uniform distribution would be 
> the best. It means that you have similar number of observations for each 
> part of you sampled gradient...for that explanatory variable.  
> 
Theoretically, the best distribution (in terms of power) is to have a 
bimodal distribution, with values either at their maximum or minimum. 
However, this design makes it impossible to check whether the 
relationship is linear or not.  I mention this because I forgot to 
mention that linearity is assumed, and this is more important.  It's 
also easy to check - plot the residuals against the predictor.  If they 
look curved, then it suggests that the relationship is not linear, and 
that would be a reason to transform.

Bob

-- 
Bob O'Hara

Dept. of Mathematics and Statistics
P.O. Box 68 (Gustaf Hällströmin katu 2b)
FIN-00014 University of Helsinki
Finland

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