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Unformatted text preview: ce 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. 11327 Chapter 01  An Introduction to Business Statistics 85. 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. Determine the 95% confidence interval for this point estimate and interpret its meaning. 11328 Chapter 01  An Introduction to Business Statistics 11329 Chapter 01  An Introduction to Business Statistics 86. 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. Calculate the 95% prediction interval for this point estimate. 11330 Chapter 01  An Introduction to Business Statistics 11331 Chapter 01  An Introduction to Business Statistics 87. 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 = 5.465v 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% prediction interval for the point estimate given above is from 33.9 to 65.74. Interpret
the meaning of this interval. 11332 Chapter 01  An Introduction to Business Statistics 11333 Chapter 01  An Introduction to Business Statistics 88. 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...
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 Winter '14

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