forecasting part 2 - Jonathan Stromp Forecasting...

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Jonathan Stromp Forecasting Assignment- Part 2(Spring 2011).pdf CF Auto Company
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Section A: Introduction Mr. Ayres, the inventory manager at CF, needs to replenish the company’s inventory of sport utility vehicles (SUVs) by placing the April, 2011 order with various manufactures. To help Mr. Ayres make a better decision, I have been hired to provide an interpretation of the regression model to forecast the number of SUVs sold per month and insight concerning managerial questions. The variables used in the forecasting equation are shown below in table 1. Table 1: Variable Names and Definitions Name Variable Description (all are monthly values) Sales Number of sport utility vehicles sold TV Television advertising expenses ($1,000s) Newspaper Newspaper advertising expenses ($1,000s) Futures Average price of gasoline futures market ($ per barrel) Associates Average number of sales associates working per day Bonus Average value of bonus bucks ($ per SUV sold) Price Average list price of sport utility vehicles sold ($) Promotion 1 for month with a sales promotion; 0 if no sales promotion Section B: Forecasting Model Interpretation 1. Quality The independent variables that are included in the final regression model at the 10% significance level are TV, Associates, Bonus, and Promotion. These variables are considered significant because all of their p-values are less than 10%. Then the entire model is proven valid because the Significance-F is also less than 10%. The coefficient of determination, or R-Square, is a very common statistic used to evaluate how well the model fits. In other words the R- Squared value provides a measure of how well future outcomes are likely to be predicted by the model. In our case the model has an R-Square value of .503, which means that 50.3% of the
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forecasting part 2 - Jonathan Stromp Forecasting...

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