This chapter presents an Excel-based regression analysis to forecast seasonal demand for U.S. Imported Beer sales data. The following seasonal regression models are presented and interpreted including a simple yearly model, a quarterly model, a semi-annual model, and a monthly model. The results of the models are compared and a discussion of each model's efficacy is provided. The yearly model does the best at forecasting U.S. Import Beer sales. However, the yearly does not provide a window into shorter-term (i.e., monthly) forecasting periods and subsequent peaks and valleys in demand. Although the monthly seasonal regression model does not explain as much variance in the data as the yearly model it fits the actual data very well. The monthly model is considered a good forecasting model based on the significance of the regression statistics and low mean absolute percentage error. Therefore, it can be concluded that the monthly seasonal model presented is doing an overall good job of forecasting U.S. Import Beer Sales and assisting managers in shorter time frame forecasting.
Kros, J. and Keller, C. (2010), "Seasonal regression forecasting in the U.S. beer import market", Lawrence, K. and Klimberg, R. (Ed.) Advances in Business and Management Forecasting (Advances in Business and Management Forecasting, Vol. 7), Emerald Group Publishing Limited, Bingley, pp. 73-96. https://doi.org/10.1108/S1477-4070(2010)0000007008Download as .RIS
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