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Techniques for analyzing the relationship between one (or more) "dependent" variables and "independent" variables.

1
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The only issue here is an assumption violation; i.e. correlated errors. You would probably be better served using autoregressive or moving-average methods rather than a simple linear extrapolation.
answered Mar 18 '13 by ReliableResearch
0
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I only use the LR for determining the signficance of including or excluding a particular variable or block of variables. I do not have an explicit example, but there are some other ways to assess mo …
answered Mar 29 '13 by ReliableResearch
7
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Yes, it is possible. Couple of things here. The direction of your predictors are critical to the interpretation; if they were scaled in the opposite direction, they would be positive. Second, it would …
answered Mar 1 '13 by ReliableResearch
4
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I think the key here is understanding the missing data mechanism; or at least ruling some out. Building seperate models is akin to treating missing and non-missing groups as random samples. If missing …
answered Mar 1 '13 by ReliableResearch
0
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coefficients. In terms of outliers, there are quite a few approaches to how to handle them. One thing you can always try when there are a few outliers is run the regression with and without them...and compare the results. …
answered Jun 12 '13 by ReliableResearch