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SCENARIO 13-1 a Large National Bank Charges Local Companies for Using

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SCENARIO 13-1 A large national bank charges local companies for using their services.A bank official reported the results of a regression analysis designed to predict the bank's charges (Y) -- measured in dollars per month -- for services rendered to local companies.One independent variable used to predict service charges to a company is the company's sales revenue (X) -- measured in millions of dollars.Data for 21 companies who use the bank's services were used to fit the model: SCENARIO 13-1 A large national bank charges local companies for using their services.A bank official reported the results of a regression analysis designed to predict the bank's charges (Y) -- measured in dollars per month -- for services rendered to local companies.One independent variable used to predict service charges to a company is the company's sales revenue (X) -- measured in millions of dollars.Data for 21 companies who use the bank's services were used to fit the model: The results of the simple linear regression are provided below. two-tail p value =0.034 (for testing -Referring to Scenario 13-1, interpret the p-value for testing whether exceeds 0. A) There is sufficient evidence (at the = 0.05) to conclude that sales revenue (X) is a useful linear predictor of service charge (Y) . B) There is insufficient evidence (at the = 0.10) to conclude that sales revenue (X) is a useful linear predictor of service charge (Y) . C) Sales revenue (X) is a poor predictor of service charge (Y) . D) For every $1 million increase in sales revenue, you expect a service charge to increase $0.034. The results of the simple linear regression are provided below. SCENARIO 13-1 A large national bank charges local companies for using their services.A bank official reported the results of a regression analysis designed to predict the bank's charges (Y) -- measured in dollars per month -- for services rendered to local companies.One independent variable used to predict service charges to a company is the company's sales revenue (X) -- measured in millions of dollars.Data for 21 companies who use the bank's services were used to fit the model: The results of the simple linear regression are provided below. two-tail p value =0.034 (for testing -Referring to Scenario 13-1, interpret the p-value for testing whether exceeds 0. A) There is sufficient evidence (at the = 0.05) to conclude that sales revenue (X) is a useful linear predictor of service charge (Y) . B) There is insufficient evidence (at the = 0.10) to conclude that sales revenue (X) is a useful linear predictor of service charge (Y) . C) Sales revenue (X) is a poor predictor of service charge (Y) . D) For every $1 million increase in sales revenue, you expect a service charge to increase $0.034. SCENARIO 13-1 A large national bank charges local companies for using their services.A bank official reported the results of a regression analysis designed to predict the bank's charges (Y) -- measured in dollars per month -- for services rendered to local companies.One independent variable used to predict service charges to a company is the company's sales revenue (X) -- measured in millions of dollars.Data for 21 companies who use the bank's services were used to fit the model: The results of the simple linear regression are provided below. two-tail p value =0.034 (for testing -Referring to Scenario 13-1, interpret the p-value for testing whether exceeds 0. A) There is sufficient evidence (at the = 0.05) to conclude that sales revenue (X) is a useful linear predictor of service charge (Y) . B) There is insufficient evidence (at the = 0.10) to conclude that sales revenue (X) is a useful linear predictor of service charge (Y) . C) Sales revenue (X) is a poor predictor of service charge (Y) . D) For every $1 million increase in sales revenue, you expect a service charge to increase $0.034. two-tail p value =0.034 (for testing SCENARIO 13-1 A large national bank charges local companies for using their services.A bank official reported the results of a regression analysis designed to predict the bank's charges (Y) -- measured in dollars per month -- for services rendered to local companies.One independent variable used to predict service charges to a company is the company's sales revenue (X) -- measured in millions of dollars.Data for 21 companies who use the bank's services were used to fit the model: The results of the simple linear regression are provided below. two-tail p value =0.034 (for testing -Referring to Scenario 13-1, interpret the p-value for testing whether exceeds 0. A) There is sufficient evidence (at the = 0.05) to conclude that sales revenue (X) is a useful linear predictor of service charge (Y) . B) There is insufficient evidence (at the = 0.10) to conclude that sales revenue (X) is a useful linear predictor of service charge (Y) . C) Sales revenue (X) is a poor predictor of service charge (Y) . D) For every $1 million increase in sales revenue, you expect a service charge to increase $0.034.
-Referring to Scenario 13-1, interpret the p-value for testing whether SCENARIO 13-1 A large national bank charges local companies for using their services.A bank official reported the results of a regression analysis designed to predict the bank's charges (Y) -- measured in dollars per month -- for services rendered to local companies.One independent variable used to predict service charges to a company is the company's sales revenue (X) -- measured in millions of dollars.Data for 21 companies who use the bank's services were used to fit the model: The results of the simple linear regression are provided below. two-tail p value =0.034 (for testing -Referring to Scenario 13-1, interpret the p-value for testing whether exceeds 0. A) There is sufficient evidence (at the = 0.05) to conclude that sales revenue (X) is a useful linear predictor of service charge (Y) . B) There is insufficient evidence (at the = 0.10) to conclude that sales revenue (X) is a useful linear predictor of service charge (Y) . C) Sales revenue (X) is a poor predictor of service charge (Y) . D) For every $1 million increase in sales revenue, you expect a service charge to increase $0.034. exceeds 0.


A) There is sufficient evidence (at the SCENARIO 13-1 A large national bank charges local companies for using their services.A bank official reported the results of a regression analysis designed to predict the bank's charges (Y) -- measured in dollars per month -- for services rendered to local companies.One independent variable used to predict service charges to a company is the company's sales revenue (X) -- measured in millions of dollars.Data for 21 companies who use the bank's services were used to fit the model: The results of the simple linear regression are provided below. two-tail p value =0.034 (for testing -Referring to Scenario 13-1, interpret the p-value for testing whether exceeds 0. A) There is sufficient evidence (at the = 0.05) to conclude that sales revenue (X) is a useful linear predictor of service charge (Y) . B) There is insufficient evidence (at the = 0.10) to conclude that sales revenue (X) is a useful linear predictor of service charge (Y) . C) Sales revenue (X) is a poor predictor of service charge (Y) . D) For every $1 million increase in sales revenue, you expect a service charge to increase $0.034. = 0.05) to conclude that sales revenue (X) is a useful linear predictor of service charge (Y) .
B) There is insufficient evidence (at the SCENARIO 13-1 A large national bank charges local companies for using their services.A bank official reported the results of a regression analysis designed to predict the bank's charges (Y) -- measured in dollars per month -- for services rendered to local companies.One independent variable used to predict service charges to a company is the company's sales revenue (X) -- measured in millions of dollars.Data for 21 companies who use the bank's services were used to fit the model: The results of the simple linear regression are provided below. two-tail p value =0.034 (for testing -Referring to Scenario 13-1, interpret the p-value for testing whether exceeds 0. A) There is sufficient evidence (at the = 0.05) to conclude that sales revenue (X) is a useful linear predictor of service charge (Y) . B) There is insufficient evidence (at the = 0.10) to conclude that sales revenue (X) is a useful linear predictor of service charge (Y) . C) Sales revenue (X) is a poor predictor of service charge (Y) . D) For every $1 million increase in sales revenue, you expect a service charge to increase $0.034. = 0.10) to conclude that sales revenue (X) is a useful linear predictor of service charge (Y) .
C) Sales revenue (X) is a poor predictor of service charge (Y) .
D) For every $1 million increase in sales revenue, you expect a service charge to increase $0.034.

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