TABLE 13-01A 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 charge 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 = β0 + β1X+εThe results of the simple linear regression are provided below.  = -2,700 + 20X, SYX = 65, two-tailed p value = 0.034 (for testing  β1)Referring to Table 13-1, a 95% confidence interval for β1 is (15, 30). Interpret the interval.

A regression analysis between weight (y in pounds) and height (x in inches) resulted in the following least squares line:  = 120 + 5x. This implies that if the height is increased by 1 inch, the weight is expected to:

TABLE 13-6The following EXCEL tables are obtained when “Score received on an exam (measured in percentage points)” (Y) is regressed on “percentage attendance” (X) for 22 students in a Statistics for Business and Economics course.        Referring to Table 13-6, which of the following statements is true?

TABLE 13-2A candy bar manufacturer is interested in trying to estimate how sales are influenced by the price of their product. To do this, the company randomly chooses 6 small cities and offers the candy bar at different prices. Using candy bar sales as the dependent variable, the company will conduct a simple linear regression on the data below:   SUMMARY OUTPUT Regression Statistics Multiple R 0.885404 R Square 0.783941 Adjusted R Square 0.729926 Standard Error 16.29861 Observations 6 ANOVA   df SS MS F Significance F Regression 1 3855.422 3855.422 14.51346 0.018946 Residual 4 1062.578 265.6446 Total 5 4918         Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 161.3855 26.16069 6.16901 0.003506 88.75183 234.0193 88.75183 234.0193 price -48.1928 12.65017 -3.80965 0.018946 -83.3153 -13.0703 -83.3153 -13.0703 Referring to the above Table, to test whether a change in price will have any impact on average sales, what would be the critical values? Use α = 0.05.

TABLE 13-2A candy bar manufacturer is interested in trying to estimate how sales are influenced by the price of their product. To do this, the company randomly chooses 6 small cities and offers the candy bar at different prices. Using candy bar sales as the dependent variable, the company will conduct a simple linear regression on the data below:   SUMMARY OUTPUT Regression Statistics Multiple R 0.885404 R Square 0.783941 Adjusted R Square 0.729926 Standard Error 16.29861 Observations 6 ANOVA   df SS MS F Significance F Regression 1 3855.422 3855.422 14.51346 0.018946 Residual 4 1062.578 265.6446 Total 5 4918         Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 161.3855 26.16069 6.16901 0.003506 88.75183 234.0193 88.75183 234.0193 price -48.1928 12.65017 -3.80965 0.018946 -83.3153 -13.0703 -83.3153 -13.0703 Referring to the above Table, what is the standard error of the estimate, Sε, for the data?

TABLE 13-11A company that has the distribution rights to home video sales of previously released movies would like to use the box office gross (in millions of dollars) to estimate the number of units (in thousands of units) that it can expect to sell. Following is the output from a simple linear regression along with the residual plot and normal probability plot obtained from a data set of 30 different movie titles:   ANOVA  Referring to Table 13-11, which of the following is the correct interpretation for the slope coefficient?

TABLE 13-2A candy bar manufacturer is interested in trying to estimate how sales are influenced by the price of their product. To do this, the company randomly chooses 6 small cities and offers the candy bar at different prices. Using candy bar sales as the dependent variable, the company will conduct a simple linear regression on the data below:   SUMMARY OUTPUT Regression Statistics Multiple R 0.885404 R Square 0.783941 Adjusted R Square 0.729926 Standard Error 16.29861 Observations 6 ANOVA   df SS MS F Significance F Regression 1 3855.422 3855.422 14.51346 0.018946 Residual 4 1062.578 265.6446 Total 5 4918         Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 161.3855 26.16069 6.16901 0.003506 88.75183 234.0193 88.75183 234.0193 price -48.1928 12.65017 -3.80965 0.018946 -83.3153 -13.0703 -83.3153 -13.0703 Referring to the above Table, what is the standard error of the regression slope estimate, Sb1?

THE NEXT QUESTIONS ARE BASED ON THE FOLLOWING INFORMATION:The manager of a used-car dealership is very interested in the resale price of used cars. The manager feels that the age of the car is important in determining the resale value. He collects data on the age and resale value of 15 cars and runs a regression analysis with the value of the car (in thousands of dollars) as the dependent variable and the age of the car (in years) as the independent variable. Unfortunately, he spilled his coffee on the printout and lost some of the results, identified by “A” through “F”. The partial results left are displayed below.What is the value of *F*?

TABLE 13-8It is believed that GPA (grade point average, based on a four point scale) should have a positive linear relationship with ACT scores. Given below is the Excel output from regressing GPA on ACT scores using a data set of 8 randomly chosen students from a Big-Ten university.  Regressing GPA on ACT   ANOVA  Referring to Table 13-8, what are the decision and conclusion on testing whether there is any linear relationship at 1% level of significance between GPA and ACT scores?

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