PLEASE ANSWER USING EXCEL AND SHOW ALL STEPS

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 521 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