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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 11397 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 11398 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 11399 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 11400 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 11401 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. 11402 Chapter 01  An Introduction to Business Statistics
AACSB: Analytic
Bloom's: Application
Difficulty: Hard
Learning Objective: 1
Topic: Multiple regression model 11403 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 11404 Chapter 01  An Introduction to Business Statistics
AACSB: Analytic
Bloom's: Application
Difficulty: Hard
Learning Objective: 1
Topic: Multiple regression model 11405 Chapter 01  An Introduction to Business Statistics...
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 Winter '14

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