# How can I increase the $R^2$ -value of my linear model? Should I increase it? [duplicate]

I'm constructing a linear model from a data set with 10 variables and my current "best" model uses 4 variables. I've tested the variables and not all of them show significance, so the most that I might add to the model might be 5 variables overall. However, with 4 and 5 variables, eventhough I get very good Pr(>|t|) values for all the variables that I've added and while my $R^2$ value has been improving, it's still just "only" $0.4287$ and the scale is $[0,1]$.

Should I be happy with $< 0.5$ $R^2$ value of should I aim to improve it in order to improve my model even more? What can I do to increase the $R^2$ value now that I have already added all variables that I can add as predictors? Should I look into interaction terms? Or something else?

• A high Rsquared is not everything. You may have a look at a discussion R-squared versus adjusted Rsquared. Oct 15 '16 at 15:20
• Use another criterion to adjust your model, like, AIC. This won't improve R^2 but will help variable selection. Oct 15 '16 at 16:03
• @jchaykow How to do that in R? Oct 15 '16 at 16:51
• @mavavilj I've done this method in my blog: rcode.io/work/2016-04-04-FF Scroll down to forward selection and backward selection. Oct 17 '16 at 0:39