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
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