When the goal of using regression is simply predictive modeling, we often don't care about which particular predictors go into our model, so long as the final model yields the best possible predictions.
A naïve (and awful) approach is to use all the independent variables available to try to model the dependent variable. Let's try this approach by trying to predict mpg from every other variable in the mtcars dataset, using the following code:
# the period after the squiggly denotes all other variables
model <- lm(mpg ~ ., data=mtcars)
summary(model)
Call:
lm(formula = mpg ~ ., data = mtcars)
Residuals:
Min 1Q Median 3Q Max
-3.4506 -1.6044 -0.1196 1.2193 4.6271
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12.30337 18.71788 0.657 0.5181
cyl -0.11144 1.04502 -0.107 0.9161
disp 0.01334 0.01786 0.747 0.4635
hp -0.02148 0.02177...
