Date: Mon, 19 Sep 2005 18:55:09 +0200
Reply-To: Karl Koch <TheRanger@gmx.net>
Sender: "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From: Karl Koch <TheRanger@gmx.net>
Subject: General questions: Linear Regression
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I have a few questions which I would like to ask here regarding linear
regression analysis in SPSS. I have performed a linear regression with three
IVs and one DV.
I would like to find the regression function that models best the data in
order to make predictions. I have 3 IVs but only 2IVs do stat. sig.
contribute to the variation of the DV.
I did a normal (simultanious) linear regression. I get the following model
with its coefficients (The ANOVA table tells me that this model is
Model B t sig.
1 (Constant) 4.200 58.972 .000
FactorA -.779 -18.288 .000
FactorB -.022 -.622 .535
FactorC -1.601 -25.350 .000
Furthermore, the model summary tells me an R square of 0.30 which means that
the model accounts for 30 % of the variance in the DV.
Now some questions:
1) How does this translate to the regression function Y = alpha + beta1 *
FactorA + beta2 * FactorC ? I only got one R square value for the ENTIRE
2) Where can I find out how much a R square of 0.30 (30%) really means? Is
this a strong effect? Can somebody provide me with some approaches of how
this could be interpreted?
3) When performing the regression analysis, SPSS offers in the "Save" dialog
box the "Mahalanobis" distance. Does somebody here know more details about
this option - I could not find a lot in the help... The reason why I am
asking is that one book suggests to tick this box without further
explaination and I usually want to know that I am messing up :-)
I am somehow missing the link between the SPSS results and the more
theoretical knowledge in the books. Perhaps somebody more experineced here
can help me out?
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