Module 5: Regression Modeling Part 2

February 13, 2023

Review:

• Regression Modeling Part 1
• MLB
• East Side Real Estate

Presentation:

• Video Lecture
• Variable Creation
  • Demonstrate with Sample Real Estate data
  • Multicollinearity
  • Interaction Variables
    • combine separate variables mathematically
    • add, multiply, etc
    • another way to avoid problems with multicollinearity
  • 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

Assignment:

• Multiple Regression Analysis
  • Select the worksheet corresponding to the last digit of your PID from this file: PuebloRealEstate_Exam2Data_ByPID
  • Save a copy of the data and convert to .xlsx format
  • Use Excel with the Analysis Toolpak Add-in
  • Build a regression model to estimate selling price
    • You must use 1 interaction variable
    • You must use 1 or more Dummy variables
    • Experiment with multiple combinations of variables to find the best model, maximizing and balancing R^2 and the F Statistic
  • Generate selling price forecasts for the following 4 Pueblo properties (with neighborhood location)
    • 4 bed, 3 ba, 2,931 sqft (Belmont)
    • 2 bed, 1 ba, 1,650 sqft (Central High School)
    • 3 bed, 2 ba, 1391 sqft (Northside/Avenues)
    • 5 bed, 3 ba, 3070 sqft (Regency and Meadows)
  • Submit your Excel spreadsheet (.xlsx) here.