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.
Cet exercice fait partie du cours
Intermediate Regression with statsmodels in Python
Instructions
- 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.
Exercice interactif pratique
Essayez cet exercice en complétant cet exemple de code.
# 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()