# Use the following information to answer question1-4.Reviewing her performance on her last ten (10)..

Use the following information to answer question1-4.Reviewing her performance on her last ten (10) three foot putts, Lisa see the following pattern:Make, Make, Miss, Make, Miss, Make, Make, Make, Miss1. Enter a one?1?for every make and zero (0) for every miss. What is the mean of this sample of putts?a. 3/5b. b. 7/10c. c. 3/2d. d. 1/2e. e. 3/12. What is the conditional probability of a make on a put following a miss?a. 2/3b. 1/10c. 1/2d. 1/5e. 4/53. What is the conditional probability that Lisa made the putt given that she made the two previous putts?a. 0b. 1/3c. 2/5d. 1e. 2/34. How many runs are there?a. 3b. 5c. 6d. 10e. 4Frustrated with her performance. Lisa practices 100 putts. She calculates 42 runs. The W-W runs test gives the following information.Expected number of Runs:49; sd; 4.7737; z-score=-1.46645. At the 5% level of significance, which of the following is true?a. The number of runs is not significantly different from the expected number of runs.b. The number of runs is significantly less than the expected number of runs.c. The number of runs is not significantly different from zerod. The number of runs is significantly less than zeroe. The number of runs is significantly greater than the expected umber of runs.6. Does the data provide evidence that Lisa has a hot hand?a. Yes, because the number of runs is significantly less than the expected number of runs.b. Yes, because the number of runs is significantly greater than the expected number of runs.c. No, because the number of runs is significantly less than the expected number of runs.d. No, because the number of runs is significantly greater than the expected number of runs.e. No, because the number of runs is not significantly different than the expected number of runs.7. The z-score means that the number of runs is roughlya. 1.4664 standard deviations below the expected number of runs.b. About 1.47% percent likely to be significantly different from the expected number of runs.c. 1.4664 more than the expected number of runs.d. 1.4664 standard deviations above the expected number of runs.e. 1.4664 less than the expected number of runs.Lisa is also worried about her consistency on the golf course. She thinks that she has â€œ post-birdie syndromeâ€. This condition shows up on the hole following a birdie (one under par on a golf hole). She thinks that she becomes overconfident and score worse ( remeber that in golf, a higher score is worse!) Like a good economist, she has gathered data to test her theory.Score par or below Score above par(success) ( failure)Hole following a birdie 11 22Hole not following a birdie 62 548. What test should she perform to determine whether she has post-birdie syndrome?a. Multiple Regressionb. Standard Deviation testc. Runs testd. Single regressione. Chi Square test9. Which best describes the hypothesis that she is testing ( the alternative hypothesis, Not the null hypothesis)?a. â€œ On average, I score lower ( I get a better score) after a birdie than I do otherwiseâ€b. â€œI have a hot handâ€c. â€œI do not have a hot handâ€d. â€œOn average, I score higher( I get a worse score) after birdie than I do otherwiseâ€e. â€œOn average, I score the same after a birdie as I do otherwiseâ€Lisa runs the appropriate test, which returns a p-value of 0.03210. What does this p-value mean?a. If the null hypothesis is true, then Lisaâ€™s probability of making a type II error is 96.8%(0.968)b. If the null hypothesis is correct, the probability of observing this much or more difference between scores is 3.2%(0.032)c. The correlation coefficient for her scores is 0.032.d. Her score after a birdie is 3.2% higher than her score before a birdie.e. If the null hypothesis is true, then Lisaâ€™s probability of making a type II error is 3.2% (0.032)11. Suppose that Lisa had decided on a 5% significance level. Does she conclude that she has post-birdie syndrome?a. Yes, because she scores lower after a birdie.b. Impossible to tell, because we do not have the Z-statistic.c. Yes, because her p-value is less than 5%d. No, because her p-value is less than5%e. No, because her p-value is less than the 5% critical value of 1.96.12. Suppose that we divided her scores into groups of five, if we plotted the frequency distribution of number of scores in each group of five that were above par, we would plot aa. Binomial distributionb. Geometric distributionc. Studentâ€™s t-distributiond. Normal distributione. Chi square distributionHeight is measured in inches, BMI is body Mass index (weight in kilograms divided by height in meters squared). Wonderlic is the score on an intelligence test, 40 yard dash is measured in seconds and Division I-AA dummy answers the question, â€œ was your college in division I-AA? (1 for yes and 0 for a no)13. What is the dependent variable?a. Constantb. Draft positionc. Division I-AA dummyd. Height14. Is the coefficient on Height statistically different from zero at the 5% significance level?a. Yes, because the coefficient is more than1.96% times as large as the standard error in absolute value.b. Yes, because the p-value is smaller than the coefficient.c. No, because its standard error is very large in absolute value (higher than any other standard error.d. Yes, because neither the coefficient nor the standard error equals zero.e. No, because the standard error is negative.15. What is the t0statistic for testing whether the coefficient on Height is different from zero?a. (-19.55)*(-4.24)b. -19.55c. (-19.55)/(-4.24)d. (-19.55)/(-4.24)e. -4.2416. After controlling for all other independent variables in the regression, what is the effect of Height on draft position?a. A one inch increase in height leads to an increase in draft position ( Later in the draft) of 19.55 spots.b. A one inch increase in height leads to a decrease in draft position ( earlier in the draft) of 19.55 spots.c. A one inch increase in height leads to an increase in draft position ( later in the draft) of 4.24 spots.d. A one inch increase in height leads to a decrease in draft position ( earlier in the draft) of 4.25 spots.e. There is no statistical relationship between height and draft position at the 5% significance level.17. Consider two divisions I-AA quarterbacks with the same height, BMI, and Wonderlic score. One has a 40 yard dash of 4.5 seconds, while the other has a 40 yard dash of 5.5 seconds. The model predicts that the FASTER quarterback with roughlya. 129 positions earlier.b. 129 positions later.c. 3 positions earlier.d. 3 positions latere. There is no statistical relationship between 40 yard dash and draft position at the 5% significance level.18. The 95% confidence interval for the estimate of the effect on draft position of playing at a Division I-AA college is abouta. 0 to 55.96b. 49.47 to 62.45c. 52.65 to 59.27d. 16.91 to 185.23e. 3.31 to 55.96In a recent paper â€œCatching a Draft: on the process of selecting quarterbacks in the NFL draft, â€œ Berri and Simmons try to explain the draft position of quarterbacks, which is a number from1 (1st pick in the 1 st round) ro250 (last pick in the last round). They present the following regression table.Variable IConstant 4963.033.04Height -19.55-4.24BMI -272.67-2.42BMI squared 4.682.33Wonderlic -1.94-1.8240 yard dash 128.813.16Division I-AA dummy 55.963.3119. After controlling for height, Wonderlic score, 40 yard dash time, and Division I-AA status, does the model predict that players with higher BMI will always be drafter later than players with lower BMI?a. trueb. false