Read the questions and choose the best answer. _31006_1 f904ede5566e85dd95e0d168cd2d6212 https://az545770.vo.msecnd.net/lti/exam

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 13-9It is believed that, the average numbers of hours spent studying per day (HOURS) during undergraduate education should have a positive linear relationship with the starting salary (SALARY, measured in thousands of dollars per month) after graduation. Given below is the Excel output from regressing starting salary on number of hours spent studying per day for a sample of 51 students. NOTE: Some of the numbers in the output are purposely erased.   ANOVA  Referring to Table 13-9, the 90% confidence interval for the average change in SALARY (in thousands of dollars) as a result of spending an extra hour per day studying is

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, the normality of error assumption appears to have been violated.

TABLE 13-7An investment specialist claims that if one holds a portfolio that moves in the opposite direction to the market index like the S&P 500, then it is possible to reduce the variability of the portfolio’s return. In other words, one can create a portfolio with positive returns but less exposure to risk. A sample of 26 years of S&P 500 index and a portfolio consisting of stocks of private prisons, which are believed to be negatively related to the S&P 500 index, is collected. A regression analysis was performed by regressing the returns of the prison stocks portfolio (Y) on the returns of S&P 500 index (X) to prove that the prison stocks portfolio is negatively related to the S&P 500 index at a 5% level of significance. The results are given in the following EXCEL output. Referring to Table 13-7, to test whether the prison stocks portfolio is negatively related to the S&P 500 index, the measured value of the test statistic is

TABLE 13-9It is believed that, the average numbers of hours spent studying per day (HOURS) during undergraduate education should have a positive linear relationship with the starting salary (SALARY, measured in thousands of dollars per month) after graduation. Given below is the Excel output from regressing starting salary on number of hours spent studying per day for a sample of 51 students. NOTE: Some of the numbers in the output are purposely erased.   ANOVA  Referring to Table 13-9, the value of the measured t-test statistic to test whether average SALARY depends linearly on HOURS is

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:

Which one of the following equations is False?    Note: ·{“version”:”1.1″,”math”:”·”} means multiplication.

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 *B*?

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 *D*?