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

question 134

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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:
Yi = β\beta 0 + β\beta 1 Xi + ε\varepsilon i
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: Y<sub>i </sub>= \beta <sub>0</sub> + \beta <sub>1</sub> X<sub>i </sub>+ \varepsilon  <sub>i</sub> The results of the simple linear regression are provided below.   ? </sup>= -2,700 +20 X ,S<sub>Y</sub><sub>X</sub><sub> </sub><sub> </sub>= 65,two-tail p value = 0.034 (for testing  \beta <sub>1</sub>)     -Referring to Scenario 13-1,interpret the p-value for testing whether  \beta <sub>1 </sub>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.
? = -2,700 +20 X ,SYX = 65,two-tail p value = 0.034 (for testing β\beta 1)
11eb129a_22de_eb6b_935a_932306595b21_TB6723_00
-Referring to Scenario 13-1,interpret the p-value for testing whether β\beta 1 exceeds 0.


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