Problem on using multiple linear regression for forecasting

The following problem shows students how using indicator variables can help model seasonal demand patterns and improve forecasting ability.

The data in the following data files (in SPSS, text, and Excel format, respectively): BIKESALES.SAV, BIKESALES.TXT, BIKESALES.XLS, contains quarterly sales of a particular mountain bike for the previous four years at a bicycle shop in Switzerland. The bike sales exhibit a positive trend but with strong seasonal patterns, with bike sales being higher in the spring and summer quarters than in the winter and fall quarters. The shop owner wishes to forecast sales for next year to ensure that there is sufficient inventory to meet demand.

1. How can you display the data graphically to demonstrate the seasonal pattern in sales?
2. If you fit a simple linear regression model with "sales" as the response (dependent) variable and "period" as the predictor (independent) variable, how can you tell this model fits poorly?
3. Fit a multiple linear regression model that incorporates the seasonality. (Hint: consider dummy indicator variables.)
4. How many bikes should be stocked each quarter of the coming year to be reasonably sure of meeting demand? (Hint: consider the relative costs of over-estimating and under-estimating demand.)

Last updated: June, 2008

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© 2008, Iain Pardoe, Lundquist College of Business, University of Oregon