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.

Bu egzersiz

Intermediate Regression with statsmodels in Python

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

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.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# 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()

Kodu Düzenle ve Çalıştır