The Ladies Professional Golfers Association (LPGA) maintains statistics on performance and earnings for members of the LPGATour. Year-end performance statistics for the 30 players who had the highest total earnings in LPGA Tour events for 2005 appear on the data disk in the file named LPGATour (www.lpga.com, 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.
Click on the webfile logo to reference the data.
Develop an estimated regression equation that can be used to predict the total earnings given the average number of putts taken on greens hit in regulation (to the nearest whole number).
Earnings (000s) = + PuttAvg
What is the SSE associated with this model (to the nearest whole number)?
What is the value of the coefficient of determination (to 3 decimals)? Note: report R2 between 0 and 1.
Develop an estimated regression equation that can be used to predict the total earnings by adding two independent variables to the regression equation in part (a). The added variables are the percentage of time a player is able to hit the green in regulation and the percentage of time a player is able to make a sand save by getting "up and down" once in a greenside bunker (to the nearest whole number).
Earnings (000s) = + PuttAvg
+ GreensReg + SandSaves
What is the SSE associated with this model (to the nearest whole number)?
What is the value of the coefficient of determination (to 3 decimals)? Note: report R2 between 0 and 1.
Using = .05, test whether the two independent variables added in part (b) contributed significantly to the estimated regression equation.
What is the value of the F test statistic (to 2 decimals)?
What is the p-value?
Selectless than .01between .01 and .025between .025 and .05between .05 and .10greater than .10Item 12
What is your conclusion about the two variables GreensReg and SandSaves?
SelectConclude that these variables contribute significantly to the modelCannot conclude that these variables contribute significantly to the modelItem 13
In general, low scores should lead to high earnings. To investigate this option, develop a simple linear regression that will predict total earnings based on average score (to the nearest whole number).
Earnings (000s) = + ScoreAvg
What is the value of the coefficient of determination (to 3 decimals)? Note: report R2 between 0 and 1.
Which regression model considered in this exercise would you prefer to use?
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
