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.
この演習はコースの一部です
Intermediate Regression with statsmodels in Python
実践的なインタラクティブ演習
このサンプルコードを完成させて、この演習に挑戦してみましょう。
# Extract the model coefficients, coeffs
coeffs = ____
# Print coeffs
print(coeffs)
# Assign each of the coeffs
____, ____, ____, ____ = ____