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This question already has an answer here:

As far as I know first I have to standardize the variables. Then I have to check whether they are normal or not, then I should check whether there is multicolinearity. Then I perform the make regression and check whether the residuals are randomly distributed or not.

Is there anything I am missing or have wrong?

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marked as duplicate by whuber May 7 '13 at 21:28

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You do not have to standardize the variables; you do not have to check them for normality. You should check for collinearity. The residuals should be normally distributed and not related to the independent variables.

Beyond that there is a whole lot to do. There is the whole issue of model selection, for one. You need to check for outliers. There is more, too.

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There's no need to make assumptions about the distribution of the predictors. If a predictor is heavily skewed towards higher values, you may need to transform it though.

Watch out for multicolinearity (hint: VIF). Always make sure your residuals are normally distributed, if they're not - do something. Transforms of the predictors can be worth trying out.

EDIT: Removed first line of post (wrong info). Check the later answers for information regarding standardization of the predictors.

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  • $\begingroup$ What software standardizes the predictors for you? R does not, SAS does not and I am pretty sure SPSS does not. At least, not by default. Nor is it necessary to do so (although some people think it is a good idea). Also, condition indexes are a better method for collinearity than VIFs are. $\endgroup$ – Peter Flom May 7 '13 at 21:27
  • $\begingroup$ That is debatable. Some agree with you (including some very prominent people) but I don't. I think changes in the original units are usually easier to understand. But it's not an assumption of regression, in any case. $\endgroup$ – Peter Flom May 7 '13 at 21:41
  • $\begingroup$ As I remember it, it's a good idea to standardize the predictors in order to understand what they're doing for the response. The interpretation of the parameter estimated will be clearer. I thought they all standardized them by default, if it's wrong I'll edit my post. $\endgroup$ – Eric Paulsson May 7 '13 at 21:42
  • $\begingroup$ No assumption at all, he asked for it though and I believe it's can be good idea to standardize (especially if you're new to regression). In the end, it comes down to the nature of whatever you're modeling. $\endgroup$ – Eric Paulsson May 7 '13 at 21:52

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