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Date:         Mon, 19 Sep 2005 18:55:09 +0200
Reply-To:     Karl Koch <>
Sender:       "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From:         Karl Koch <>
Subject:      General questions: Linear Regression
Content-Type: text/plain; charset="us-ascii"

Hello all,

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 significant.):

Coefficients ----------- 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 model...

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?

Kind Regards, Karl

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