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Exercise

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.

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  • Fit a linear regression of house price versus the number of convenience stores and the square-root of the distance to the nearest MRT stations, without an interaction or intercept, using the taiwan_real_estate dataset.
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