Including an interaction
Just as in the case with one numeric and one categorical explanatory variable, it is possible for numeric explanatory variables to interact. With this model structure, you'll get a third slope coefficient: one for each explanatory variable and one for the interaction.
Here, you'll run, predict, and plot the same model as in the previous exercise, but this time including an interaction between the explanatory variables.
この演習はコースの一部です
Intermediate Regression with statsmodels in Python
演習の手順
- Change the name of the model to
mdl_price_vs_conv_dist_inter. - Adapt the rest of the code from the previous exercise to include an interaction.
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。
# Convert to mdl_price_vs_conv_dist_inter
mdl_price_vs_conv_dist = ols("price_twd_msq ~ n_convenience + sqrt_dist_to_mrt_m", data=taiwan_real_estate).fit()
# Use mdl_price_vs_conv_dist_inter to make predictions
n_convenience = np.arange(0, 11)
sqrt_dist_to_mrt_m = np.arange(0, 81, 10)
p = product(n_convenience, sqrt_dist_to_mrt_m)
explanatory_data = pd.DataFrame(p, columns=["n_convenience", "sqrt_dist_to_mrt_m"])
prediction_data = explanatory_data.assign(
price_twd_msq = mdl_price_vs_conv_dist.predict(explanatory_data))
sns.scatterplot(x="n_convenience", y="sqrt_dist_to_mrt_m", data=taiwan_real_estate, hue="price_twd_msq", legend=False)
sns.scatterplot(x="n_convenience", y="sqrt_dist_to_mrt_m", data=prediction_data, hue="price_twd_msq", marker="s")
plt.show()