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Unformatted text preview: 425 Chapter 01 - An Introduction to Business Statistics 100. Consider the following partial computer output for a multiple regression model. Analysis of Variance What is the total sum of squares? 5855.86 SS Total = 2270.11 + 3585.75 = 5855.86 AACSB: Analytic Bloom's: Application Difficulty: Easy Learning Objective: 2 Topic: Multiple regression model 101. Consider the following partial computer output for a multiple regression model. Analysis of Variance What is the explained variation? 2270.11 SS Regression = explained variation = 2270.11 AACSB: Analytic Bloom's: Application Difficulty: Easy Learning Objective: 4 Topic: Multiple regression model 1-1426 Chapter 01 - An Introduction to Business Statistics 102. Consider the following partial computer output for a multiple regression model. Analysis of Variance What is the mean square error? 137.92 MSE = 3585.75/26 = 137.92 AACSB: Analytic Bloom's: Application Difficulty: Easy Learning Objective: 2 Topic: Multiple regression model 103. Consider the following partial computer output for a multiple regression model. Analysis of Variance Calculate R2. 0.3877 R2 = 2270.11/5855.86 = 0.3877 AACSB: Analytic Bloom's: Application Difficulty: Medium Learning Objective: 3 Topic: Coefficient of determination 1-1427 Chapter 01 - An Introduction to Business Statistics 104. Consider the following partial computer output for a multiple regression model. Analysis of Variance Test the overall usefulness of the model at α = .01. Calculate F and make your decision. F = 8.23, reject H0 MSR = 2270.11/2 = 1135.06 MSE = 3585.75/26 = 137.92 F = MSR/MSE = 1135.06/137.92 = 8.23 F.01,2,26 = 5.53 8.23 > 5.53, reject H0 AACSB: Analytic Bloom's: Application Difficulty: Hard Learning Objective: 4 Topic: Overall F test 1-1428 Chapter 01 - An Introduction to Business Statistics 105. Consider the following partial computer output for a multiple regression model. Analysis of Variance What is the number of observations in the sample? n = 2 + 26 + 1 = 29 AACSB: Analytic Bloom's: Application Difficulty: Medium Learning Objective: 1 Topic: Multiple regression model 106. Consider the following partial computer output for a multiple regression model. Analysis of Variance Calculate the adjusted R2. 0.3407 R2 = 2270.11/5855.86 = 0.3877 R2 adjusted = (0.3877 - (2/28) (28/26) = (0.3877 - 0.714) (1.077) = 0.3407 AACSB: Analytic Bloom's: Application Difficulty: Medium Learning Objective: 3 Topic: Coefficient of determination 1-1429 Chapter 01 - An Introduction to Business Statistics 107. Consider the following partial computer output for a multiple regression model. Analysis of Variance Write the least squares prediction equation. ŷ = 41.225 + 1.081x1 - 18.404x2 AACSB: Analytic Bloom's: Application Difficulty: Easy Learning Objective: 1 Topic: Multiple regression model 1-1430 Chapter 01 - An Introduction to Business Statistics 108. Consider the following partial computer output for a multiple regression model. Analysis of Variance Test the usefulness of variable x2 in the model at your conclusions. = .05. Calculate the t statistic and state t = -4.048. We reject H0 and conclude that x2 is making a significant contribution to predicting y. t-025,26 = -2.056 t = -18.404/4.547 = -4.048 -4.048 < -2.056, reject H0 AACSB: Analytic Bloom's: Application Difficulty: Hard Learning Objective: 5 Topic: Significance of an independent variable 1-1431 Chapter 01 - An Introduction to Business Statistics 109. Consider the following partial computer output for a multiple regression model. Analysis of Variance Determine the 95% interval for β2 and interpret its meaning (-27.75, -9.055) We are 95% confident that as β2 increases by 1 unit, the value of y will decrease by at least 9.055 units and decrease at most by 27.753 units (b2 ± tsb2) = (-18.404 ± 2.056 (4.547)) = (-18.404 ± 9.349) = -27.753 to -9.055 AACSB: Analytic Bloom's: Application Difficulty: Hard Learning Objective: 5 Topic: Significance of an independent variable 1-1432 Chapter 01 - An Introduction to Business Statistics 110. A member of the state legislature has expressed concern about the differences in the mathematics test scores of high school freshmen across the state. She asks her research assistant to conduct a study to investigate what factors could account for the differences. The research assistant looked at a random sample of school districts across the state and used the factors of percentage of mathematics teachers in each district with a degree in mathematics, the average age of mathematics teachers and the average salary of mathematics teachers s = 7.62090 Analysis of Variance What is the total sum of squares? 2911.59 SS Total = 1053.09 + 1858.50 = 2911.59 AACSB: Analytic Bloom's: Application Difficulty: Easy Learning Objective: 2 Topic: Multiple regression model 1-1433 Chapter 01 - An Introduction to Business Statistics 111. A member of the state legislature has expressed concern about the differences in the mathematics test scores of high school freshmen across...
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This document was uploaded on 01/20/2014.

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