From: John Sorkin <jsorkin_at_grecc.umaryland.edu>

Date: Mon 11 Apr 2005 - 22:43:35 EST

Date: Mon 11 Apr 2005 - 22:43:35 EST

Manuel,

The problem you describe does not sound like it is due to
multicolinearity. I state this because you variance inflation factor is
modest (1.1) and, more importantly, the correlation between your
independent variables (x1 and x2) is modest, -0.25. I suspect the
problem is due to one, or more, observations having a disproportionally
large influence on your coefficients. I suggest you plot your residuals
vs. predicted values. I would also do a formal analysis of the influence
each observation has on the reported coefficients. You might consider
computing Cook's distance for each observation.

I hope this has helped.

John

John Sorkin M.D., Ph.D.

Chief, Biostatistics and Informatics

Baltimore VA Medical Center GRECC and

University of Maryland School of Medicine Claude Pepper OAIC

University of Maryland School of Medicine
Division of Gerontology

Baltimore VA Medical Center

10 North Greene Street

**GRECC (BT/18/GR)
**

Baltimore, MD 21201-1524

410-605-7119

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jsorkin@grecc.umaryland.edu

>>> Manuel Gutierrez <manuel_gutierrez_lopez@yahoo.es> 4/11/2005 6:22:55 AM >>>

x1 normal(0,1) x2 normal(0,1) Impact of perturbations on coefficients: mean s.d. min max (Intercept) -26.067 0.270 -27.235 -25.481 x1 0.726 0.025 0.672 0.882 x2 0.060 0.011 0.037 0.082

I get a mean for x1 of 0.726 which is closer to what
is expected.

I am not an statistical expert so I'd like to know if
my evaluation of the effects of collinearity is
correct and in that case any solutions to obtain a
reliable linear model.

Thanks,

Manuel

Some more detailed information:

> A<-lm(y~x1+x2)

*> summary(A)
*

Call:

lm(formula = y ~ x1 + x2)

Residuals:

Min 1Q Median 3Q Max -4.221946 -0.484055 -0.004762 0.397508 2.542769

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) -27.23472 0.27996 -97.282 < 2e-16 *** x1 0.88202 0.02475 35.639 < 2e-16 *** x2 0.08180 0.01239 6.604 2.53e-10 ***

--- Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1 Residual standard error: 0.823 on 241 degrees of freedom Multiple R-Squared: 0.8411, Adjusted R-squared: 0.8398 F-statistic: 637.8 on 2 and 241 DF, p-value: < 2.2e-16Received on Tue Apr 12 09:45:44 2005

> cor.test(x1,x2)

Pearson's product-moment correlation data: x1 and x2 t = -3.9924, df = 242, p-value = 8.678e-05 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: -0.3628424 -0.1269618 sample estimates: cor -0.248584 ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html [[alternative HTML version deleted]] ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html

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