(e) Based on weather forecasts indicating that next week's mean temperature will be 80°, the retail outlet uses the regression to forecast its sales next week. What will be its forecast? Compute a 95 percent interval for the outlet's sales next week. (4)
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Suppose that the retail outlet suspects that its sales depend on the price of its air conditioners as well as on the mean temperature. For the randomly chosen 12 weeks, it obtains data regarding the price of an air cond II h t d ·t Iitioner as we as on t e mean emperature an IS temperature (degrees) price (hundreds of dollars) sales (number of air conditioners) 72 2 77 2 82 1 43 2 31 2 28 2 81 1 83 2 76 2 60 1 50 1 55 1 and regress sales on temperature and price: .
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(f) What is the percentage of the sample variation of the sales values that is explained by the least squares prediction equation? (1)
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(g) Formally, conduct the Ftest of model usefulness. (5)
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(h) How much of an increase in weekly sales can be expected if the mean temperature goes up 1° and the price of an air conditioner is held constant? Is this effect statistically significant? (i) How much of an increase in weekly sales can be expected if the price of air conditioner goes down by $1 and the mean temperature remains constant? Is this effect statistically significant? (2) (j) Suppose that the retail outlet is convinced that the price elasticity of demand for its air conditioners is constant when the price of an air conditioner is between $100 and $300. If this is true, is the regression equation of the right mathematical form? If not, what alternative form would you suggest? (3)
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 Summer '19
 Regression Analysis