Modeling two numeric explanatory variables
You already saw how to make a model and predictions with a numeric and a categorical explanatory variable. The code for modeling and predicting with two numeric explanatory variables is the same, other than a slight difference in how to specify the explanatory variables to make predictions against.
Here you'll model and predict the house prices against the number of nearby convenience stores and the square-root of the distance to the nearest MRT station.
taiwan_real_estate is available with the square-root transformed variable sqrt_dist_to_mrt_m. itertools.product is also loaded.
This exercise is part of the course
Intermediate Regression with statsmodels in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Fit linear regression of price vs. no. of conv. stores and sqrt dist. to nearest MRT, no interaction
mdl_price_vs_conv_dist = ____
# See the result
print(mdl_price_vs_conv_dist.params)