Plot observed vs predicted and residual vs predicted for each
regression. You can do this regardless of how complicated each
regression is. R-square is the square of the correlation between
observed and predicted for plain regression, so such graphs make
explicit what underlies each R-square.
With a difference of means that is an order of magnitude, you may be
better off modelling on some different scale, e.g. -glm, link(log)-.
You do also need to check whether predictors play similar roles in each case.
Nick
On Tue, Jan 15, 2013 at 12:00 PM, Birk Teuchert <b.teuchert@gmx.de> wrote:
> I have problems identifying the reasons for the differing results for 2 different time periods (the general regression equation is the same for both periods) i would like to do my analysis seperatly with.
> How can I identify possible reasons for the differing results?
>
> I had a look at the descriptives and indeed I noticed that mean (10 times higher) and SD (4 times hihger) of my dependent is a lot higher for one time period which results in an R2 of 30% compared to 4% in the other time period.
>
> Is this difference in the distribution of the dependent variable enough to explain the different results or how do I identify possible other explanations??
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