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B. Fail to reject the null hypothesis 11182 Chapter 01  An Introduction to Business Statistics 116. 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. The dealer randomly selects one of the six observations with a
monthly sales value of 8000 tires and monthly advertising expenditures of $7000. Calculate
the value of the residual for this observation.
A. 1
B. 2
C. 2
D. 1 117. 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). Residuals are calculated for
all of the randomly selected six months and ordered from smallest to largest.
Determine the normal score for the smallest residual.
A. .1053
B. 1.25
C. 1053
D. 1.25 118. 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). Residuals are calculated for
all of the randomly selected six months and ordered from smallest to largest.
Determine the normal score for the third residual in the ordered array.
A. 0.421
B. 0.2
C. 0.2
D. 0.421 11183 Chapter 01  An Introduction to Business Statistics 119. A data set with 7 observations yielded the following. Use the simple linear regression
model.
= 21.57
= 68.31
= 188.9
= 5,140.23
= 590.83
SSE = 1.06
Find the estimated slope.
A. 12.36
B. 3.13
C. 4.745
D. 8.70 120. A data set with 7 observations yielded the following. Use the simple linear regression
model.
= 21.57
= 68.31
= 188.9
= 5,140.23
= 590.83
SSE = 1.06
Find the estimated yintercept.
A. 4.745
B. 12.36
C. 9.76
D. 3.08 11184 Chapter 01  An Introduction to Business Statistics 121. A data set with 7 observations yielded the following. Use the simple linear regression
model.
= 21.57
= 68.31
= 188.9
= 5,140.23
= 590.83
SSE = 1.06
Calculate the standard error.
A. .212
B. .1514
C. .389
D. .4604 122. A data set with 7 observations yielded the following. Use the simple linear regression
model.
= 21.57
= 68.31
= 188.9
= 5,140.23
= 590.83
SSE = 1.06
Find the rejection point for the t statistic (
A. 2.015, reject null hypothesis
B. 13.993, reject null hypothesis
C. 1.358, reject null hypothesis
D. 36.460, reject null hypothesis = .05). Test H0: β1 ≤ 0 vs. Ha: β1 > 0. 11185 Chapter 01  An Introduction to Business Statistics 123. A data set with 7 observations yielded the following. Use the simple linear regression
model.
= 21.57
= 68.31
= 188.9
= 5,140.23
= 590.83
SSE = 1.06
Determine the 95% confidence interval for the average value of Y when x = 3.25.
A. (27.31 28.25)
B. (26.51 29.05)
C. (1.98 4.52)
D. (2.78 3.72) 124. A data set with 7 observations yielded the following. Use the simple linear regression
model.
= 21.57
= 68.31
= 188.9
= 5,140.23
= 590.83
SSE = 1.06
Calculate the correlation coefficient.
A. .111
B. .334
C. .974
D. .987 11186 Chapter 01  An Introduction to Business Statistics 125. A data set with 7 observations yielded the following. Use the simple linear regression
model.
= 21.57
= 68.31
= 188.9
= 5,140.23
= 590.83
SSE = 1.06
Calculate the coefficient of determination.
A. .111
B. .334
C. .974
D. .987 126. Use the least squares regression equation,
predicted value of Y when X = 3.25?
A. 15.42
B. 15.61
C. 27.78
D. 44.92 = 12.36 + 4.745(X), and determine the 127. Consider the following partial computer output from a simple linear regression analysis. What is the estimated yintercept?
A. 1.12
B. 28.13
C. 22.90
D. .99 11187 Chapter 01  An Introduction to Business Statistics 128. Consider the following partial computer output from a simple linear regression analysis. What is the estimated slope?
A. 1.12
B. 28.13
C. 22.90
D. .05 129. Consider the following partial computer output from a simple linear regression analysis. Write the equation of the least squares line.
A. ŷ = 1.12  28.13x
B. ŷ = 28.13 + 1.12
C. ŷ = 28.13 + 1.12x
D. ŷ = 1.12 + .04891x 130. Consider the following partial computer output from a simple linear regression analysis. Test H0: β1 ≤ 0 vs. Ha: β1 > 0.
A. .088, fail to reject null hypothesis
B. 22.895, reject the null hypothesis
C. .088, reject the null hypothesis
D. 22.895, fail to reject the null hypothesis 11188 Chapter 01  An Introduction to Business Statistics 131. Consider the following partial computer output from a simple li...
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 Winter '14

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