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*?

For a two-tailed test of significance of slope coefficient (β1), the appropriate null hypothesisis most likely to be:

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