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Forecasting Outside the Range of the Explanatory Variable: Chapter 3.7.2

Explore the use of regression models for predicting far outside the range of explanatory variables, with the pitfalls and challenges. Example from Olympic Long Jump regression model included.

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Forecasting Outside the Range of the Explanatory Variable: Chapter 3.7.2

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  1. Forecasting Outside the Range of the Explanatory Variable: Chapter 3.7.2

  2. Prediction • Least squares estimate of regression model: = • Predicted selling price for a house that is 1000 square feet = $50,398 • Predicted selling price for a house that is 0 square feet = -$25,222 !

  3. Extrapolation • Extrapolation: Use of the regression model to predict far outside the range of values of the explanatory variable X that you used to obtain the line. Such predictions are often not accurate.

  4. Olympic Long Jump: Length of gold medal jump (Y) vs. Year (X)

  5. Predictions from Long Jump Simple Linear Regression Model • Predicted Olympic gold medal winning long jumps: • 2008 (Beijing): -78.42771+0.053557*2008 = 29.11 feet • 2028: -78.42771+0.053557*2028 = 30.19 feet • 3000: -78.42771+0.053557*3000 = 82.24 feet

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