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Unformatted text preview: ney invested in advertising.
He randomly selects 6 months of data consisting of tire sales (in thousands of tires) and
advertising expenditures (in thousands of dollars). The equation of the least squares line is,
= 3 + 1x.
Provide a managerial interpretation of the estimated y intercept.
A. With no advertising dollars, we should expect to sale 3 tires.
B. If there is no advertising, the sales are expected to be 3000 tires.
C. For every thousand dollars in advertising expenditures, we should expect to sell 1000 tires.
D. For every thousand dollars in advertising expenditures, we should expect to sell 3000 tires. 110. A local tire dealer wants to predict the number of tires sold each month. He believes that
the number of tires sold is a linear function of the amount of money invested in advertising.
He randomly selects 6 months of data consisting of tire sales (in thousands of tires) and
advertising expenditures (in thousands of dollars). The equation of the least squares line is,
= 3 + 1x.
Provide a managerial interpretation of the estimated slope.
A. As sales of tires increase by 1000, we expect advertising expenditures to increase by
$1000.
B. If there is no advertising, the sales are expected to be 3000 tires.
C. For every additional thousand dollars in advertising expenditures, we should expect to sell
an additional 1000 tires.
D. For every additional thousand dollars in advertising expenditures, we should expect to sell
and additional 3000 tires. 11178 Chapter 01  An Introduction to Business Statistics 111. A local tire dealer wants to predict the number of tires sold each month. He believes that
the number of tires sold is a linear function of the amount of money invested in advertising.
He randomly selects 6 months of data consisting of tire sales (in thousands of tires) and
advertising expenditures (in thousands of dollars). Based on the data set with 6 observations,
the simple linear regression equation of the least squares line is = 3 + 1x. = 24
= 124
= 42
= 338
= 196
MSE = 4
Use the least squares regression equation and estimate the monthly tire sales when advertising
expenditures is $4000.
A. 4000 tires
B. 1000 tires
C. 3000 tires
D. 7000 tires 11179 Chapter 01  An Introduction to Business Statistics 112. A local tire dealer wants to predict the number of tires sold each month. He believes that
the number of tires sold is a linear function of the amount of money invested in advertising.
He randomly selects 6 months of data consisting of tire sales (in thousands of tires) and
advertising expenditures (in thousands of dollars). Based on the data set with 6 observations,
the simple linear regression equation of the least squares line is = 3 + 1x. = 24
= 124
= 42
= 338
= 196
MSE = 4
Using the sums of the squares given above, determine the 95% confidence interval for the
slope.
A. (1.951 4.049)
B. (4.552 6.552)
C. (.0492 2.0492)
D. (0.259 1.741) 11180 Chapter 01  An Introduction to Business Statistics 113. A local tire dealer wants to predict the number of tires sold each month. He believes that
the number of tires sold is a linear function of the amount of money invested in advertising.
He randomly selects 6 months of data consisting of tire sales (in thousands of tires) and
advertising expenditures (in thousands of dollars). Based on the data set with 6 observations,
the simple linear regression equation of the least squares line is = 3 + 1x. = 24
= 124
= 42
= 338
= 196
MSE = 4
Using the sums of the squares given above, determine the 90% confidence interval for the
mean value of monthly tire sales when the advertising expenditure is $5000.
A. (3.32 12.68)
B. (3.74 12.26)
C. (6.62 9.38)
D. (6.08 9.92) 11181 Chapter 01  An Introduction to Business Statistics 114. A local tire dealer wants to predict the number of tires sold each month. He believes that
the number of tires sold is a linear function of the amount of money invested in advertising.
He randomly selects 6 months of data consisting of tire sales (in thousands of tires) and
advertising expenditures (in thousands of dollars). Based on the data set with 6 observations,
the simple linear regression equation of the least squares line is = 3 + 1x. = 24
= 124
= 42
= 338
= 196
MSE = 4
Using the sums of the squares given above, determine the 90% prediction interval for an
individual month's tire sales when the advertising expenditure is $5000.
A. (3.32 12.68)
B. (3.74 12.26)
C. (6.62 9.38)
D. (6.08 9.92) 115. A local tire dealer wants to predict the number of tires sold each month. He believes that
the number of tires sold is a linear function of the amount of money invested in advertising.
He randomly selects 6 months of data consisting of monthly tire sales (in thousands of tires)
and monthly advertising expenditures (in thousands of dollars). The simple linear regression
equation is
= 3 + 1X and sample correlation coefficient (r2) = .6364. Test to determine if
there is a significant correlation between the monthly tire sales and monthly advertising
expenditures. Use H0: ρ = 0 vs. HA: ρ ≠ 0 at α = .05.
A. Reject the null hypoth...
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 Winter '14

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