Exercise

# Modeling 2 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 in 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; `dplyr`

, `tidyr`

and `ggplot2`

are loaded.

Instructions 1/3

**undefined XP**

- 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, using the
`taiwan_real_estate`

dataset.