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Below is a partial multiple regression anova table

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Unformatted text preview: the number of independent variables in the model and the mean squared error. 298, 5, and 74.952 AACSB: Analytic Bloom's: Application Difficulty: Medium Learning Objective: 2 Topic: Multiple regression model 1-1397 Chapter 01 - An Introduction to Business Statistics 78. Below is a partial multiple regression computer output. Test the overall usefulness of the model at decision. = .01. Calculate the F statistic and make your F = 87.4532, reject H0, It appears that the multiple regression model is significant and at least one of the predictor variables is significantly related to the dependent variable. AACSB: Analytic Bloom's: Application Difficulty: Medium Learning Objective: 4 Topic: Overall F test 79. Below is a partially completed multiple regression analysis of variance (ANOVA) table. Calculate R2. .60 AACSB: Analytic Bloom's: Application Difficulty: Easy Learning Objective: 3 Topic: Coefficient of determination 1-1398 Chapter 01 - An Introduction to Business Statistics 80. Below is a partial multiple regression computer output. Write the least squares prediction equation. = 22.02-.18x1-.25x2- 4.69x3 + 3.67x4 + 22.32x5 AACSB: Analytic Bloom's: Application Difficulty: Easy Learning Objective: 1 Topic: Multiple regression model 1-1399 Chapter 01 - An Introduction to Business Statistics 81. Below is a partial multiple regression computer output. Test the usefulness of variable x5 in the model at α = .05. Calculate the t statistic and state your conclusions. t = 6.2. We reject H0, and conclude that x5 is making a significant contribution in predicting y. AACSB: Analytic Bloom's: Application Difficulty: Medium Learning Objective: 5 Topic: Significance of an independent variable 1-1400 Chapter 01 - An Introduction to Business Statistics 82. Below is a partial multiple regression computer output. Determine the 95% interval for β4 and interpret its meaning. (2.886 to 4.454) We are 95% certain that as x4 increase by one unit, the value of the dependent variable will increase at least by 2.886 units and at most by 4.454 units. AACSB: Analytic Bloom's: Application Difficulty: Medium Learning Objective: 5 Topic: Significance of an independent variable 1-1401 Chapter 01 - An Introduction to Business Statistics 83. 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 Interpret the regression coefficients for the variables advertising, price and store. For each additional hundred dollars of advertising, the light fixture sales are estimated to increase by an average of 820 units. For each additional dollar of increase in the price of the fixture, the number of fixtures sold is estimated to decrease by an average 330 units. For each additional retail store used to sell the light fixture, the sales are estimated to increase by an average of 1840 units, if the value of the independent variable is within the experimental region and the other two independent variables are held constant. 1-1402 Chapter 01 - An Introduction to Business Statistics AACSB: Analytic Bloom's: Application Difficulty: Hard Learning Objective: 1 Topic: Multiple regression model 1-1403 Chapter 01 - An Introduction to Business Statistics 84. 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, estimate the monthly light fixture sales and calculate the value of the residual, if the company spends $4000 on advertising, the price of the fixture is $60 and the fixture is being sold at 3 retail stores. Estimated sales = 49,820 units and residual = -7.82 or -7820 units 1-1404 Chapter 01 - An Introduction to Business Statistics AACSB: Analytic Bloom's: Application Difficulty: Hard Learning Objective: 1 Topic: Multiple regression model 1-1405 Chapter 01 - An Introduction to Business Statistics...
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