Скачать или смотреть The Ladies Professional Golfers Association (LPGA) maintains statistics on performance and earnings…

The Ladies Professional Golfers Association (LPGA) maintains statistics on performance and earnings for members of the LPGA Tour. Year-end performance statistics for 134 golfers for 2014 appear in the file named 2014LPGAStats3. Earnings (1,000s) is the total earnings in thousands of dollars; Scoring Avg. is the average score for all events; Greens in Reg. is the percentage of time a player is able to hit the greens in regulation; Putting Avg. is the average number of putts taken on greens hit in regulation; and Drive Accuracy is the percentage of times a tee shot comes to rest in the fairway. A green is considered hit in regulation if any part of the ball is touching the putting surface and the difference between the value of par for the hole and the number of strokes taken to hit the green is at least 2. (a) Develop an estimated regression equation that can be used to predict the average score for all events given the average number of putts taken on greens hit in regulation. Use x1 for Putting Avg. (Round your numerical values to two decimal places.) Å· = (b) Develop an estimated regression equation that can be used to predict the average score for all events given the percentage of time a player is able to hit the greens in regulation, the average number of putts taken on greens hit in regulation, and the percentage of times a player #x27;s tee shot comes to rest in the fairway. Use x2 for Greens in Reg. and x3 for Drive Accuracy. (Round your numerical values to two decimal places.) Å· = (c) At the 0.05 level of significance, test whether the model in part (b) is overall significant. State the null and alternative hypotheses. H0: One or more of the parameters is not equal to zero. Ha: ð ›½1 = ð ›½2 = ð ›½3 = ð ›½4 = 0 Find the value of the test statistic. (Round your answer to two decimal places.) Find the p-value. (Round your answer to three decimal places.) p-value = Is the model in part (b) significant? Reject H0. We conclude that the model in part (b) is significant. Do not reject H0. We conclude that the model in part (b) is significant. Do not reject H0. We conclude that the model in part (b) is not significant. Reject H0. We conclude that the model in part (b) is not significant. (d) Backward elimination is a variable selection technique used in multiple regression analysis that removes one variable at a time until all slope coefficients in the model have p-values that do not exceed a given threshold value. Consider the model you developed in part (b), and use backward elimination with a p-value threshold of 0.2 to develop an improved estimated regression equation that can be used to predict the average score for all events. Keep the same variable names used in part (b). (Round your numerical values to two decimal places.) Å· =

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