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.
Este exercício faz parte do curso
Intermediate Regression with statsmodels in Python
Exercício interativo prático
Experimente este exercício completando este código de exemplo.
# Extract the model coefficients, coeffs
coeffs = ____
# Print coeffs
print(coeffs)
# Assign each of the coeffs
____, ____, ____, ____ = ____