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.
This exercise is part of the course
Intermediate Regression in R
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Fit a 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
mdl_price_vs_conv_dist