1.- An old saying in golf is \"you drive for show and you putt for dough.\" The point is that good putting is more important than long driving for shooting low scores and hence winning money. To see if this is the case, data on the top 69 money winners on the PGA tour in 1993 are examined. The average number of putts per hole for each player is used to predict their total winnings using the simple linear regression model 1993 winningsi = 0 + 1(average number of putts per hole)i + i where the deviations i are assumed to be independent and normally distributed with mean 0 and standard deviation . This model was fit to the data using the method of least squares. The following results were obtained from statistical software: R2 = 0.081 s = 281,777 Variable Constant Avg. Putts Parameter Estimate 7,897,179 -4,139,198 Std. Err. of Parameter Est. 3,023,782 1,698,371 Suppose the researchers test the hypotheses: H0: 1 = 0, Ha: 1 < 0 The value of the t statistic for this test is: a) b) c) d) 2.61 2.44 -2.44 0.081 2.- Are the inflation rates of the United States and the United Kingdom associated? If so, can we attempt to predict the U.S. Inflation Rate using the UK Inflation Rate? Suppose we fit the following simple linear regression model U.S. Inflation Ratei = 0 + 1(UK Inflation Rate)i + i where the deviations i were assumed to be independent and normally distributed, with mean 0 and standard deviation . This model was fit to the data using the method of least squares. A random sample of 20 annual rates was selected from the past 110 years' rates. The following results were obtained from statistical software. R2 = 0.533 s = 3.88795 Variable Constant UK Inflation Rate Parameter Est. 0.2030 0.6652 Std. Err. of Parameter Est. 1.087 0.1468 The intercept of the least-squares regression line is (approximately): a) b) c) d) 0.203 3.888 1.087 0.665 3.- A random sample of 79 companies from the Forbes 500 list (which actually consists of nearly 800 companies) was selected, and the relationship between sales (in hundreds of thousands of dollars) and profits (in hundreds of thousands of dollars) was investigated by regression. The following simple linear regression model was used Profitsi = 0 + 1(Sales)i + i where the deviations i were assumed to be independent and normally distributed, with mean 0 and standard deviation . This model was fit to the data using the method of least squares. The following results were obtained from statistical software: R2 = 0.662 s = 466.2 Variable Constant Sales Parameter Est. -176.644 0.092498 Std. Err. of Parameter Est. 61.16 0.0075 A 90% confidence interval for the slope 1 in the simple linear regression model is (approximately): a) b) c) d) -0.09 0.012 0.09 0.0075 -0.09 0.0075 0.09 0.012 1.- An old saying in golf is \"you drive for show and you putt for dough.\" The point is that good putting is more important than long driving for shooting low scores and hence winning money. To see if this is the case, data on the top 69 money winners on the PGA tour in 1993 are examined. The average number of putts per hole for each player is used to predict their total winnings using the simple linear regression model 1993 winningsi = 0 + 1(average number of putts per hole)i + i where the deviations i are assumed to be independent and normally distributed with mean 0 and standard deviation . This model was fit to the data using the method of least squares. The following results were obtained from statistical software: R2 = 0.081 s = 281,777 Variable Constant Avg. Putts Parameter Estimate 7,897,179 -4,139,198 Std. Err. of Parameter Est. 3,023,782 1,698,371 Suppose the researchers test the hypotheses: H0: 1 = 0, Ha: 1 < 0 The value of the t statistic for this test is: a) b) c) d) 2.61 2.44 -2.44 0.081 2.- Are the inflation rates of the United States and the United Kingdom associated? If so, can we attempt to predict the U.S. Inflation Rate using the UK Inflation Rate? Suppose we fit the following simple linear regression model U.S. Inflation Ratei = 0 + 1(UK Inflation Rate)i + i where the deviations i were assumed to be independent and normally distributed, with mean 0 and standard deviation . This model was fit to the data using the method of least squares. A random sample of 20 annual rates was selected from the past 110 years' rates. The following results were obtained from statistical software. R2 = 0.533 s = 3.88795 Variable Constant UK Inflation Rate Parameter Est. 0.2030 0.6652 Std. Err. of Parameter Est. 1.087 0.1468 The intercept of the least-squares regression line is (approximately): a) b) c) d) 0.203 3.888 1.087 0.665 3.- A random sample of 79 companies from the Forbes 500 list (which actually consists of nearly 800 companies) was selected, and the relationship between sales (in hundreds of thousands of dollars) and profits (in hundreds of thousands of dollars) was investigated by regression. The following simple linear regression model was used Profitsi = 0 + 1(Sales)i + i where the deviations i were assumed to be independent and normally distributed, with mean 0 and standard deviation . This model was fit to the data using the method of least squares. The following results were obtained from statistical software: R2 = 0.662 s = 466.2 Variable Constant Sales Parameter Est. -176.644 0.092498 Std. Err. of Parameter Est. 61.16 0.0075 A 90% confidence interval for the slope 1 in the simple linear regression model is (approximately): a) b) c) d) -0.09 0.012 0.09 0.0075 -0.09 0.0075 0.09 0.012