Session Ready
Exercise

Impact of location

The impact of location brings us to a modeling question: should we keep this variable in our model? In a later course, you will learn how we can conduct formal hypothesis tests to help us answer that question. In this course, we will focus on the size of the effect. Is the impact of location big or small?

One way to think about this would be in terms of the practical significance. Is the value of the coefficient large enough to make a difference to your average person? The units are in dollars so in this case this question is not hard to grasp.

Another way is to examine the impact of location in the context of the variability of the other variables. We can do this by building our parallel planes in 3D and seeing how far apart they are. Are the planes close together or far apart? Does the East variable clearly separate the data into two distinct groups? Or are the points all mixed up together?

Instructions
100 XP
  • Use plot_ly to draw 3D scatterplot for Price as a function of Food, Service, and East by mapping the z variable to the response and the x and y variables to the numeric explanatory variables. Use color to indicate the value of East. Place Food on the x-axis and Service on the y-axis.
  • Use add_surface() (twice) to draw two planes through the cloud of points, one for restaurants on the West side and another for restaurants on the East side. Use the objects plane0 and plane1.
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