Fit a linear model

We'll now fit a linear model, because they are simple and easy to understand. Once we've fit our model, we can see which predictor variables appear to be meaningfully linearly correlated with the target, as well as their magnitude of effect on the target. Our judgment of whether or not predictors are significant is based on the p-values of coefficients. This is using a t-test to statistically test if the coefficient significantly differs from 0. The p-value is the percent chance that the coefficient for a feature does not differ from zero. Typically, we take a p-value of less than 0.05 to mean the coefficient is significantly different from 0.

Fit the linear model (using the .fit() method) and save the results in the results variable.
Print out the results summary with the .summary() function.
Print out the p-values from the results (the .pvalues property of results).
Make predictions from the train_features and test_features using the .predict() function of our results object.

Exercise

Fit a linear model

Instructions