The Ladies Professional Golfers Association (LPGA) maintains statistics on performance and earnings for members of the LPGA Tour. Year-end performance statistics for the 30 players who had the highest total earnings in LPGA Tour events for 2005 appear in the file named LPGATour (LPGA Tour website, 2006). Earnings ($1000) 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 green in regulation; Putting Avg. is the average number of putts taken on greens hit in regulation; and Sand Saves is the percentage of time a player is able to get “up and down” once in a greenside sand bunker. 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.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.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 green in regulation, the average number of putts taken on greens hit in regulation, and the percentage of time a player is able to get “up and down” once in greenside sand bunker.At the 0.05 level of significance, test whether the two independent variables added in part (b), the percentage of time a player is able to hit the green in regulation and the percentage of time a player is able to get “up and down” once in a greenside sand bunker, contribute significantly to the estimated regression equation developed in part (a). Explain.PlayerEarnings ($1000)Scoring Avg.Greens in Reg.Putting Avg.Sand SavesAnnika Sorenstam258869.330.7721.750.595Paula Creamer153270.980.7271.750.468Cristie Kerr136170.860.7221.760.362Lorena Ochoa120271.390.6971.750.31Jeong Jang113271.170.711.790.485Natalie Gulbis101071.240.7091.780.343Meena Lee87072.320.6861.820.422Hee-Won Han85671.310.7071.780.444Gloria Park84271.430.71.790.426Catriona Matthew77771.460.6961.780.443Candie Kung75471.520.7021.850.393Marisa Baena74571.920.6841.790.446Birdie Kim71573.160.6791.860.386Soo-Yun Kang71171.80.6311.770.581Lorie Kane69972.280.7181.840.475Heather Bowie67771.460.7421.820.455Wendy Ward67572.140.7071.810.413Pat Hurst63471.470.7091.770.36Christina Kim62171.660.7181.820.307Rosie Jones61571.580.6621.80.435Carin Koch61271.590.6991.790.408Liselotte Neumann60771.470.6791.810.322Mi Hyun Kim58471.650.6741.80.25Juli Inkster57971.330.7011.790.375Michele Redman54071.590.6861.810.386Jennifer Rosales51471.850.7051.810.417Karrie Webb50071.520.7091.810.353Sophie Gustafson48572.590.6511.810.389Young Kim47171.70.6781.790.292Karine Icher45272.130.7281.760.222

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Answered: Greens in Reg. is the percentage of…

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

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The Ladies Professional Golfers Association (LPGA) maintains statistics on performance and earnings for members of the LPGA Tour. Year-end performance statistics for the 30 players who had the highest total earnings in LPGA Tour events for 2005 appear in the file named LPGATour (LPGA Tour website, 2006). Earnings ($1000) 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 green in regulation; Putting Avg. is the average number of putts taken on greens hit in regulation; and Sand Saves is the percentage of time a player is able to get “up and down” once in a greenside sand bunker. 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.
1. 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.
2. 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 green in regulation, the average number of putts taken on greens hit in regulation, and the percentage of time a player is able to get “up and down” once in greenside sand bunker.
3. At the 0.05 level of significance, test whether the two independent variables added in part (b), the percentage of time a player is able to hit the green in regulation and the percentage of time a player is able to get “up and down” once in a greenside sand bunker, contribute significantly to the estimated regression equation developed in part (a). Explain.

Player	Earnings ($1000)	Scoring Avg.	Greens in Reg.	Putting Avg.	Sand Saves
Annika Sorenstam	2588	69.33	0.772	1.75	0.595
Paula Creamer	1532	70.98	0.727	1.75	0.468
Cristie Kerr	1361	70.86	0.722	1.76	0.362
Lorena Ochoa	1202	71.39	0.697	1.75	0.31
Jeong Jang	1132	71.17	0.71	1.79	0.485
Natalie Gulbis	1010	71.24	0.709	1.78	0.343
Meena Lee	870	72.32	0.686	1.82	0.422
Hee-Won Han	856	71.31	0.707	1.78	0.444
Gloria Park	842	71.43	0.7	1.79	0.426
Catriona Matthew	777	71.46	0.696	1.78	0.443
Candie Kung	754	71.52	0.702	1.85	0.393
Marisa Baena	745	71.92	0.684	1.79	0.446
Birdie Kim	715	73.16	0.679	1.86	0.386
Soo-Yun Kang	711	71.8	0.631	1.77	0.581
Lorie Kane	699	72.28	0.718	1.84	0.475
Heather Bowie	677	71.46	0.742	1.82	0.455
Wendy Ward	675	72.14	0.707	1.81	0.413
Pat Hurst	634	71.47	0.709	1.77	0.36
Christina Kim	621	71.66	0.718	1.82	0.307
Rosie Jones	615	71.58	0.662	1.8	0.435
Carin Koch	612	71.59	0.699	1.79	0.408
Liselotte Neumann	607	71.47	0.679	1.81	0.322
Mi Hyun Kim	584	71.65	0.674	1.8	0.25
Juli Inkster	579	71.33	0.701	1.79	0.375
Michele Redman	540	71.59	0.686	1.81	0.386
Jennifer Rosales	514	71.85	0.705	1.81	0.417
Karrie Webb	500	71.52	0.709	1.81	0.353
Sophie Gustafson	485	72.59	0.651	1.81	0.389
Young Kim	471	71.7	0.678	1.79	0.292
Karine Icher	452	72.13	0.728	1.76	0.222

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