[R] Improvement of Regression Model

[Vignesh Prajapati]

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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