Fitting a parallel slopes linear regression

In Introduction to Regression with statsmodels in Python, you learned to fit linear regression models with a single explanatory variable. In many cases, using only one explanatory variable limits the accuracy of predictions. To truly master linear regression, you need to be able to fit regression models with multiple explanatory variables.

The case when there is one numeric explanatory variable and one categorical explanatory variable is sometimes called a "parallel slopes" linear regression due to the shape of the predictions — more on that in the next exercise.

Here, you'll revisit the Taiwan real estate dataset. Recall the meaning of each variable.

Variable	Meaning
dist_to_mrt_station_m	Distance to nearest MRT metro station, in meters.
n_convenience	No. of convenience stores in walking distance.
house_age_years	The age of the house, in years, in 3 groups.
price_twd_msq	House price per unit area, in New Taiwan dollars per meter squared.

taiwan_real_estate is available.