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Based on the data set with 6 observations the simple

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Unformatted text preview: esis B. Fail to reject the null hypothesis 1-1182 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 1-1183 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 y-intercept. A. 4.745 B. 12.36 C. 9.76 D. 3.08 1-1184 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. 1-1185 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 1-1186 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 y-intercept? A. 1.12 B. -28.13 C. 22.90 D. .99 1-1187 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 1-1188 Chapter 01 - An Introduction to Business Statistics 131. Consider the following partial computer output from a simple li...
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This document was uploaded on 01/20/2014.

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