Lesson 13: Polynomial Regression

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March 16, 2017 at 10:31 am  •  Posted in s17-busad360 by  •  0 Comments

Thu, Mar 16

Review:

  • Measuring Forecast Error

Presentation:

  • Polynomial Regression
    • a form of linear regression
    • only 1 independent variable (x) and 1 dependent variable (y)
    • relationship between x and y is modelled as an nth degree polynomial.
    • 2nd degree polynomial
      • y-hat = b2*x² + b1*x + b0
    • 3rd degree polynomial
      • y-hat = b3*x³ + b2*x² + b1*x + b0
    • Etc
  • Demonstrate Polynomial Regression in Sheets
    • Real Estate Modeling Data
    • Time Series Forecasting
      • Sample data: Crude Oil Prices
      • Re-code Date to Time Period (10/2014 = 1, 11/2014 = 2, and so on)
      • Select “Time Period” and “Price” Columns
        • Insert > Chart
        • Select Scatter Plot as chart type
        • Scroll to bottom, select Trendline > Polynomial
        • Experiment with 2nd, 3rd, 4th, etc degree polynomial
        • Complete activity below with 2nd degree polynomial

Activity:

  • Calculate Price Forecasts
    • Time Periods 12,13,14,15
    • Use Polynomial Regression
    • Use Crude Oil data
  • Measure Forecast Error
    • Calculate MAD
    • See “Actual” tab in Crude Oil Prices data
    • Use actual Oil Prices listed to compare with forecasts

Assignment:

  • Have a good Spring Break!

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