Hands-On Data Analysis with Pandas

Interpreting the linear regression equation

The equation derived from a linear regression model gives coefficients to quantify the relationships between the variables. Care must be exercised when attempting to interpret these coefficients if we are dealing with more than a single regressor. In the case of multicollinearity, we can't interpret them because we are unable to hold all other regressors constant to examine the effect of a single one.

Thankfully, the regressors we used for the planet data aren't correlated, as we saw from the correlation matrix heatmap we made in the Exploratory data analysis section. So, let's get the intercept and coefficients from the fitted linear model object:

# get intercept>>> lm.intercept_-1016.9414328876608 ...