Unformatted text preview: 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. Determine the 95% interval for β1 (beta coefficient for the advertising variable).
0.4089 to 2.0493 11412 Chapter 01  An Introduction to Business Statistics AACSB: Analytic
Bloom's: Application
Difficulty: Hard
Learning Objective: 5
Topic: Significance of an independent variable 11413 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. 11414 Chapter 01  An Introduction to Business Statistics We are 95% confident that if average advertising expenditures increases by $1000, while the
price of the product and the number of stores selling the product remains constant, then the
average monthly unit sales of this fixture will decrease by at most 408 units and will increase
by at most 2049 units. AACSB: Analytic
Bloom's: Analysis
Difficulty: Hard
Learning Objective: 5
Topic: Significance of an independent variable 11415 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. 11416 Chapter 01  An Introduction to Business Statistics Reject H0. We have strong evidence that the independent variable "price" has a significant
positive relationship with "Sales" in the regression model. AACSB: Analytic
Bloom's: Analysis
Difficulty: Medium
Learning Objective: 5
Topic: Significance of an independent variable 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...
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 Winter '14
 Frequency, Frequency distribution, Histogram, AACSB, Statistical charts and diagrams

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