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82 or 49820 units this point estimate is calculated

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Unformatted text preview: .84 Stores S = 5.465 R - Sq = 96.7% R - Sq(adj) = 95.0% Analysis of Variance Based on the multiple regression model given above, the point estimate of the monthly light fixture sales corresponding to second sample data is 49.82 or 49,820 units. This point estimate is calculated based on the assumption that the company spends $4000 on advertising, the price of the fixture is $60 and the fixture is being sold at 3 retail stores. Additional information related to this point estimate is given below. Determine the 95% interval for β1 (beta coefficient for the advertising variable). 1-1334 Chapter 01 - An Introduction to Business Statistics 1-1335 Chapter 01 - An Introduction to Business Statistics 89. The manufacturer of a light fixture believes that the dollars spent on advertising, the price of the fixture, and the number of retail stores selling the fixture in a particular month, influence the light fixture sales. The manufacturer randomly selects 10 months and collects the following data: The sales are in thousands of units per month, the advertising is given in hundreds of dollars per month, and the price is the unit retail price for the particular month. Using MINITAB the following computer output is obtained. The regression equation is Sales = 31.0 + 0.820 Advertising - 0.325 Price + 1.84 Stores S = 5.465 R - Sq = 96.7% R - Sq(adj) = 95.0% Analysis of Variance Based on the multiple regression model given above, the point estimate of the monthly light fixture sales corresponding to second sample data is 49.82 or 49,820 units. This point estimate is calculated based on the assumption that the company spends $4000 on advertising, the price of the fixture is $60 and the fixture is being sold at 3 retail stores. Additional information related to this point estimate is given below. The 95% confidence interval for β1is from -0.4089 to 2.0493. Interpret the meaning of this interval. 1-1336 Chapter 01 - An Introduction to Business Statistics 1-1337 Chapter 01 - An Introduction to Business Statistics 90. The manufacturer of a light fixture believes that the dollars spent on advertising, the price of the fixture, and the number of retail stores selling the fixture in a particular month, influence the light fixture sales. The manufacturer randomly selects 10 months and collects the following data: The sales are in thousands of units per month, the advertising is given in hundreds of dollars per month, and the price is the unit retail price for the particular month. Using MINITAB the following computer output is obtained. The regression equation is Sales = 31.0 + 0.820 Advertising - 0.325 Price + 1.84 Stores S = 5.465 R - Sq = 96.7% R - Sq(adj) = 95.0% Analysis of Variance Based on the multiple regression model given above, the point estimate of the monthly light fixture sales corresponding to second sample data is 49.82 or 49,820 units. This point estimate is calculated based on the assumption that the company spends $4000 on advertising, the price of the fixture is $60 and the fixture is being sold at 3 retail stores. Additional information related to this point estimate is given below. Test the usefulness of variable "price" in the model using the null hypothesis H0: β2 ≤ 0, at α = 0.05, and state your conclusions. 1-1338 Chapter 01 - An Introduction to Business Statistics 91. The management of a professional baseball team is in the process of determining the budget for next year. A major component of future revenue is attendance at the home games. In order to predict attendance at home games the team statistician has used a multiple regression model with dummy variables. The model is of the form: y = β0 + β1x1 + β2x2 + β3x3 + ε where: Y = attendance at a home game x1 = current power rating of the team on a scale from 0 to 100 before the game. x2 and x3 are dummy variables, and they are defined below. x2 = 1, if weekend x2= 0, otherwise x3= 1, if weather is favorable x3= 0, otherwise After collecting the data based on 30 games from last year, and implementing the above stated multiple regression model, the team statistician obtained the following least squares multiple regression equation: The multiple regression compute output also indicated the following: Interpret the estimated model coefficient b1 1-1339 Chapter 01 - An Introduction to Business Statistics 92. The management of a professional baseball team is in the process of determining the budget for next year. A major component of future revenue is attendance at the home games. In order to predict attendance at home games the team statistician has used a multiple regression model with dummy variables. The model is of the form: y = β0 + β1x1 + β2x2 + β3x3 + ε where: Y = attendance at a home game x1 = current power rating of the team on a scale from 0 to 100 before the game. x2 and x3 are dummy variables, and they are defined below. x2 = 1, if weekend x2= 0, otherwise x3= 1, if weather is favorable x3= 0, otherwise After collecting the data based on 30 games from l...
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This document was uploaded on 01/20/2014.

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