Lesson 8: Categorical Data and Indicator Variables

February 27, 2024

Review:

  • Multicollinearity
  • Interaction Variables
  • Used vehicle multiple regression model

Presentation:

  • Pivot tables to aggregate by category
  • Indicator Variables (aka “dummy” variables)
    • incorporate categorical variables
    • binary – value is either 1 or 0
    • if 0, then the coefficient has no effect on y-hat
    • if 1, then the coefficient is added to (or subtracted from) the estimate
  • Demonstration with Sample Real Estate data
    • Use a Pivot table to summarize price by neighborhood
    • create a dummy variable for a neighborhood
    • create a dummy variable for multiple neighborhoods

Activity:

  • Use your used car model (from last lesson)
    • Incorporate an Indicator variable to your used car model (from last lesson)
    • Can you improve R^2 and/or F with an indicator variable?

Assignment:

  • Download recent Singapore real estate transaction data
    • Build a multiple regression model to estimate selling price.
    • Incorporate one or more Indicator variables to include categorical data elements

Justin

Justin Holman is CEO of Aftermarket Analytics, where he leads efforts to develop cutting edge sales forecasting and inventory optimization technology for the Automotive Aftermarket. Prior to joining Aftermarket Analytics, Justin managed corporate consulting for the Strategy & Analytics division at MapInfo Corporation, leading major projects for retail clients including The Home Depot, Darden Restaurants, Bridgestone-Firestone, Sainsbury’s and New York & Company. Before that, Justin served as Vice President of Software Development at LogicTools, now part of IBM's supply chain application software group. Justin holds a B.A. from Claremont McKenna College, a Ph.D. from the University of Oregon and an Executive Management certificate from Northwestern University's Kellogg School of Management.

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