On Thu, Jan 29, 2015 at 6:41 PM, Yan Wu <yanwu1...@gmail.com> wrote: > Hi, > > When I fit the regression model without an intercept term, R-squared tends > to much larger than the R-squared in the model with an intercept. So in this > case, what¹s a more reasonable measure of the goodness of fit for the model > without an intercept? > > Thanks a lot!! > > Yan >
I am going through the list archives and found your question. I guess it is unanswered because it is not directly related to R language per se but is more to do with time series analysis in general. In general, R square tells you only part of the story. You need to look at the t-stats of the regression coefficients to understand whether the betas from the regression are statistically significant. Further, IIRC R square always increases as more variables are added to the regression. That is why practitioners look at "adjusted r-square" instead of "r square" which account for this. So I am curious as to why your data produces less r square when you add the constant. Is it possible to upload your data somewhere so pther can take a look at it? thanks -- Kamaraju S Kusumanchi | http://raju.shoutwiki.com/wiki/Blog ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
