These questions are off topic for this list. Try a statistical list like stats.stackexchange.com.
Probably better yet, as your statistical skills sound like they are somewhat limited, consult a local statistician for help. -- Bert On Wed, Sep 5, 2012 at 7:54 AM, Vignesh Prajapati <vign...@tatvic.com> wrote: > > Hello folks, > > I am on learning phase of R. I have developed Regression Model over six > predictor variables. while development, i found my all data are not very > linear. So, may because of this the prediction of my model is not exact. > > Here is the summary of model : > Call: > lm(formula = y ~ x_1 + x_2 + x_3 + x_4 + x_5 + x_6) > > Residuals: > Min 1Q Median 3Q Max > -125.302 -26.210 0.702 26.261 111.511 > > Coefficients: > Estimate Std. Error t value Pr(>|t|) > (Intercept) 48.62944 0.27999 173.684 < 2e-16 *** > x_1 -0.67831 0.08053 -8.423 < 2e-16 *** > x_2 0.07476 0.49578 0.151 0.880143 > x_3 -0.22981 0.06489 -3.541 0.000399 *** > x_4 0.01845 0.09070 0.203 0.838814 > x_5 3.76952 0.67006 5.626 1.87e-08 *** > x_6 0.07698 0.01565 4.919 8.75e-07 *** > --- > Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > Residual standard error: 33.76 on 19710 degrees of freedom > Multiple R-squared: 0.006298, Adjusted R-squared: 0.005995 > F-statistic: 20.82 on 6 and 19710 DF, p-value: < 2.2e-16 > > I have certain questions with this model > > 1. Any way to improve the accuracy of this model? > 2.Which of the value is most useful among Residual standard error,degrees > of freedom, Multiple R-squared, Adjusted R-squared, F-statisti, p-value > for choosing best model from numbers of model ? > 3.Is it appropriate to use polynomial model with these data? > 4.In case when i am using polynomial model for regression, which degree is > most appropriate for it? > > > Thanks > Vignesh > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > -- Bert Gunter Genentech Nonclinical Biostatistics Internal Contact Info: Phone: 467-7374 Website: http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
