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
-------------- next part --------------
A non-text attachment was scrubbed...
Name: Rplot.png
Type: image/png
Size: 5765 bytes
Desc: not available
URL:
<https://stat.ethz.ch/pipermail/r-help/attachments/20120905/b49b5a62/attachment-0012.png>
-------------- next part --------------
A non-text attachment was scrubbed...
Name: Rplot01.png
Type: image/png
Size: 5248 bytes
Desc: not available
URL:
<https://stat.ethz.ch/pipermail/r-help/attachments/20120905/b49b5a62/attachment-0013.png>
-------------- next part --------------
A non-text attachment was scrubbed...
Name: Rplot02.png
Type: image/png
Size: 5755 bytes
Desc: not available
URL:
<https://stat.ethz.ch/pipermail/r-help/attachments/20120905/b49b5a62/attachment-0014.png>
-------------- next part --------------
A non-text attachment was scrubbed...
Name: Rplot03.png
Type: image/png
Size: 6310 bytes
Desc: not available
URL:
<https://stat.ethz.ch/pipermail/r-help/attachments/20120905/b49b5a62/attachment-0015.png>
-------------- next part --------------
A non-text attachment was scrubbed...
Name: Rplot04.png
Type: image/png
Size: 5461 bytes
Desc: not available
URL:
<https://stat.ethz.ch/pipermail/r-help/attachments/20120905/b49b5a62/attachment-0016.png>
-------------- next part --------------
A non-text attachment was scrubbed...
Name: Rplot05.png
Type: image/png
Size: 7994 bytes
Desc: not available
URL:
<https://stat.ethz.ch/pipermail/r-help/attachments/20120905/b49b5a62/attachment-0017.png>
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 <vignesh at 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 at 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
a) This sounds like homework. This is not a homework support forum.
b) If it is not homework, you should take one or more classes on statistics.
Your questions are more about theory than R and this is not a statistics theory
mailing list.
c) You ask questions about the use of your data, but you provide no data or
reproducible, self-contained R code, so even if it is not homework you are not
providing us with a sporting chance at understanding your questions. Read the
Posting Guide.
---------------------------------------------------------------------------
Jeff Newmiller The ..... ..... Go Live...
DCN:<jdnewmil at dcn.davis.ca.us> Basics: ##.#. ##.#. Live
Go...
Live: OO#.. Dead: OO#.. Playing
Research Engineer (Solar/Batteries O.O#. #.O#. with
/Software/Embedded Controllers) .OO#. .OO#. rocks...1k
---------------------------------------------------------------------------
Sent from my phone. Please excuse my brevity.
Vignesh Prajapati <vignesh at 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 at 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.