Lesson 13: Polynomial Regression
March 16, 2017
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!
