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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 11426 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 11427 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 11428 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 11429 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 11430 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.
t025,26 = 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 11431 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 11432 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 11433 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.
 Winter '14

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