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  3. Intermediate Regression with statsmodels in Python

Exercise

Visualizing parallel slopes

The two plots in the previous exercise gave very different predictions: one gave a predicted response that increased linearly with a numeric variable; the other gave a fixed response for each category. The only sensible way to reconcile these two conflicting predictions is to incorporate both explanatory variables in the model at once.

When it comes to a linear regression model with a numeric and a categorical explanatory variable, seaborn doesn't have an easy, "out of the box" way to show the predictions.

taiwan_real_estate is available and mdl_price_vs_both is available as a fitted model. seaborn is imported as sns and matplotlib.pyplot is imported as plt.

Instructions 1/3

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  • Get the coefficients from mdl_price_vs_both, assigning to coeffs.
  • Look at the output of coeffs.
  • Assign each of the elements of coeffs to the appropriate variable: ic_15_30, ic_30_45, slope, and ic_0_15, in the correct order.