Kitchen sink regression

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...