Programming Question

Hello,

I have a SAS Assignment that I need help in. All the attached files have the instructions.

I could not upload the .csv file so golf.csv is now golf.txt.

Thank you

DATA ANALYSIS AND REGRESSION
Assignment-2 |
Problem 1 [15pts] – to be answered by everyone
A national homebuilder builds single-family homes and condominium style townhouses.
The file housesales.txt provides information on the selling price (PRICE), lot cost (COST), type of home
(TYPE) (R=row homes home or C=condominium) and region of the country (REGION) (MW=Midwest,
S=south) for closings during one month.
a) Define the dummy variables for region and type (write them down here), and create them in
SAS. Include your code segment for recoding the variables here.
b) Analyze the association between selling price and each individual attribute (cost, type and
region) using appropriate statistics and graphs. Discuss your findings. Include the relevant
output.
c) Fit an adequate regression model for sales price as a function of lot cost, region of country, and
type of home. Remove the terms that are not significant. The final model should only contain
variables that are significantly associated with sale price. Write down the model equation.
Include the relevant output.
d) Conduct a global F-test for overall model adequacy. Write down the test hypotheses and test
statistic and discuss conclusions. Include the relevant output.
e) Analyze model residuals to check if assumptions on data are satisfied. Discuss your findings.
Include the relevant output.
f)
Discuss what the regression model indicates for the relationship between price and home type
(i.e. interpret the coefficient values).
g) Use the regression analysis to determine whether sale prices are different for the two regions?
Explain.
h) Copy and paste your FULL SAS code into the word document along with your answers.
PROBLEM 2 [10 pts] – to be answered by everyone
The file banking.txt attached to this assignment contains the full dataset. It provides data acquired from
banking and census records for different zip codes in the bank’s current market. Such information can
be useful in targeting advertising for new customers or for choosing locations for branch offices. The
data show






median age of the population (AGE)
median years of education (EDUCATION)
median income (INCOME) in $
median home value (HOMEVAL) in $
median household wealth (WEALTH) in $
average bank balance (BALANCE) in $
The goal of this exercise is to define a regression model to predict the average bank balance as a
function of the other variables.
a)
Compute correlation values of bank balance vs the other variables. Include the relevant output.
Interpret the correlation values, and discuss which variables appear to be strongly associated.
b)
Fit a regression model of balance vs the other five variables (model M1). Compute the VIF
statistics for each x-variable and analyze whether there is a problem of multicollinearity and
take appropriate action. Include the relevant output. Discuss your answer. If you had taken an
action for multicollinearity, fit the final model (Model M2). Include the outputs for both M1 and
M2 models. Which model has the largest adj-R2 value?
c)
Analyze if there are any outliers and/or influential points for your M2 model. If so, what actions
would you take to address this issue? Make sure to implement any actions you specify here.
Include the relevant outputs. Also rerun the final model, and report adj-R2 Value.
d)
Copy and paste your FULL SAS code into the word document along with your answers.
Problem 3 [10 pts]– to be answered by everyone
Analytics is used in many different sports and has become popular with the Money Ball movie. The
golf.csv dataset contains data about 196 tour players. The variables in the dataset are:
 Player’s name
 PrizeMoney = average prize money per tournament
And a set of metrics that evaluate the quality of a player’s game.
 DrivingAccuracy = percent of times a player is able to hit the fairway with his tee shot
 GIR = percent of time a player was able to hit the green within two or less than par (Greens in
Regulation)



BirdieConversion = percentage of times a player makes a birdie or better after hitting the green
in regulation
PuttingAverage = putting performance on those holes where the green was hit in regulation.
PuttsPerRound= average number of putts per round (shots played on the green)
You are asked to build a model for PrizeMoney using the remaining predictors, and to evaluate the
relative importance of each different aspects of a player’s game on the average prize money.
Note: For the non-golfers in the class, you can refer to this page for an explanation of the terms:
http://en.wikipedia.org/wiki/Glossary_of_golf
SAS Code to Import the data
*import data from file;
proc import datafile=”golf.csv” out=golf replace;
delimiter=’,’;
getnames=yes;
run;
Note:
 The data file is in CSV format
 It is delimitered with a comma
 The SAS dataset it is writing into is golf. You can change the name if you like.
a)
b)
c)
d)
e)
Create scatterplots to visualize the associations between PrizeMoney and the other 5 variables.
Discuss the patterns displayed by the scatterplot. Also, explain if the associations appear to be
linear? (you can create scatterplots or a matrix plot). Include the relevant output.
Analyze distribution of PrizeMoney, and discuss if the distribution is symmetric or skewed.
Include the relevant output.
Apply a log transformation to PrizeMoney and compute the new variable
ln_Prize=log(PrizeMoney). Analyze distribution of ln_Prize, and discuss if the distribution is
symmetric or skewed. Include the relevant output.
Fit a regression model of ln_Prize using the remaining predictors in your dataset. Apply your
knowledge of regression analysis to define a valid model to predict ln_Prize. Include the outputs
for all the questions below before you analyze them.
1) If necessary remove the non-significant variables. Remember to remove one variable at a
time (variable with largest p-value is removed first) and refit the model, until all variables
are significant.
2) Analyze residual plots to check if the regression model is valid for your data. Discuss your
analysis.
3) Analyze if there are any outliers and/or influential points. If there are points in the dataset
that need to be investigated, give one or more reason to support each point chosen. Take
appropriate action(s) to implement it. Include the relevant outputs. Discuss your answer.
4) Write down the final model equation. Discuss why this is the best model. Include all
relevant statistics/values to substantiate your answer.
Copy and paste your FULL SAS code into the word document along with your answers
Age Education Income HomeVal Wealth Balance
35.9 14.8 91033.00 183104.00 220741.00 38517.00
37.7 13.8 86748.00 163843.00 223152.00 40618.00
36.8 13.8 72245.00 142732.00 176926.00 35206.00
35.3 13.2 70639.00 145024.00 166260.00 33434.00
35.3 13.2 64879.00 135951.00 148868.00 28162.00
34.8 13.7 75591.00 155334.00 188310.00 36708.00
39.3 14.4 80615.00 181265.00 201743.00 38766.00
36.6 13.9 76507.00 149880.00 189727.00 34811.00
35.7 16.1 107935.00 276139.00 211085.00 41032.00
40.5 15.1 82557.00 182088.00 220782.00 41742.00
37.9 14.2 58294.00 123500.00 132432.00 29950.00
43.1 15.8 88041.00 194369.00 267556.00 51107.00
37.7 12.9 64597.00 119305.00 186156.00 34936.00
36 13.1 64894.00 141011.00 160017.00 32387.00
40.4 16.1 61091.00 194928.00 113559.00 32150.00
33.8 13.6 76771.00 159531.00 197264.00 37996.00
36.4 13.5 55609.00 123085.00 105582.00 24672.00
37.7 12.8 74091.00 143750.00 217869.00 37603.00
36.2 12.9 53713.00 112649.00 117441.00 26785.00
39.1 12.7 60262.00 126928.00 161322.00 32576.00
39.4 16.1 111548.00 230893.00 331009.00 56569.00
36.1 12.8 48600.00 105737.00 106671.00 26144.00
35.3 12.7 51419.00 104149.00 111168.00 24558.00
37.5 12.8 51182.00 106898.00 88370.00 23584.00
34.4 12.8 60753.00 95869.00 143115.00 26773.00
33.7 13.8 64601.00 103737.00 134223.00 27877.00
40.4 13.2 62164.00 114257.00 144038.00 28507.00
38.9 12.7 46607.00 94576.00 114799.00 27096.00
34.3 12.7 61446.00 122619.00 161538.00 28018.00
38.7 12.8 62024.00 134430.00 149351.00 31283.00
33.4 12.6 54986.00 105647.00 126929.00 24671.00
35 12.7 48182.00 114436.00 102732.00 25280.00
38.1 12.7 47388.00 92820.00 118016.00 24890.00
34.9 12.5 55273.00 102468.00 126959.00 26114.00
36.1 12.9 53892.00 92968.00 129176.00 27570.00
32.7 12.6 47923.00 104539.00 88384.00 20826.00
37.1 12.5 46176.00 92654.00 101964.00 23858.00
23.5 13.6 33088.00 105430.00 44223.00 20834.00
38 13.6 53890.00 108446.00 95013.00 26542.00
33.6 12.7 57390.00 111836.00 134434.00 27396.00
41.7 13 48439.00 100788.00 124474.00 31054.00
36.6 14.1 56803.00 149138.00 101695.00 29198.00
34.9 12.4 52392.00 93875.00 133101.00 24650.00
36.7 12.8 48631.00 95490.00 105202.00 23610.00
38.4 12.5 52500.00 105377.00 139199.00 29706.00
34.8 12.5 42401.00 106478.00 94867.00 21572.00
33.6 12.7 64792.00 116071.00 185714.00 32677.00
37 14.1 59842.00 106949.00 135329.00 29347.00
34.4 12.7 65625.00 129688.00 175000.00 29127.00
37.2 12.5 54044.00 108654.00 140726.00 27753.00
35.7 12.6 39707.00 89552.00 80124.00 21345.00
37.8 12.9 45286.00 108431.00 91928.00 28174.00
35.6 12.8 37784.00 92712.00 60721.00 19125.00
35.7 12.4 52284.00 92143.00 146028.00 29763.00
34.3 12.4 42944.00 86192.00 98778.00 22275.00
39.8 13.4 46036.00 99508.00 98343.00 27005.00
36.2 12.3 50357.00 90750.00 126613.00 24076.00
35.1 12.3 45521.00 82720.00 105346.00 23293.00
35.6 16.1 30418.00 139739.00 24999.00 16854.00
40.7 12.7 52500.00 94792.00 147222.00 28867.00
33.5 12.5 41795.00 94456.00 91806.00 21556.00
37.5 12.5 66667.00 78906.00 143750.00 31758.00
37.6 12.9 38596.00 95364.00 54453.00 17939.00
39.1 12.6 44286.00 93103.00 110465.00 22579.00
33.1 12.2 37287.00 75561.00 86591.00 19343.00
36.4 12.9 38184.00 80099.00 76438.00 21534.00
37.3 12.5 47119.00 88958.00 102993.00 22357.00
38.7 13.6 44520.00 96112.00 93915.00 25276.00
36.9 12.7 52838.00 101705.00 75040.00 23077.00
32.7 12.3 34688.00 82870.00 93750.00 20082.00
36.1 12.4 31770.00 74525.00 47446.00 15912.00
39.5 12.8 32994.00 89223.00 50592.00 21145.00
36.5 12.3 33891.00 72739.00 81880.00 18340.00
32.9 12.4 37813.00 86667.00 69643.00 19196.00
29.9 12.3 46528.00 88889.00 96591.00 21798.00
32.1 12.3 30319.00 67083.00 34367.00 13677.00
36.1 13.3 36492.00 172768.00 24999.00 20572.00
35.9 12.4 51818.00 80357.00 135185.00 26242.00
32.7 12.2 35625.00 64737.00 76321.00 17077.00
37.2 12.6 36789.00 86563.00 69764.00 20020.00
38.8 12.3 42750.00 77717.00 95192.00 25385.00
37.5 13 30412.00 138911.00 24999.00 20463.00
36.4 12.5 37083.00 70909.00 95833.00 21670.00
42.4 12.6 31563.00 81597.00 71759.00 15961.00
19.5 16.1 15395.00 67500.00 24999.00 5956.00
30.5 12.8 21433.00 83456.00 24999.00 11380.00
33.2 12.3 31250.00 91049.00 52976.00 18959.00
36.7 12.5 31344.00 77541.00 36510.00 16100.00
32.4 12.6 29733.00 60252.00 27531.00 14620.00
36.5 12.4 41607.00 76270.00 98455.00 22340.00
33.9 12.1 32813.00 40313.00 79167.00 26405.00
29.6 12.1 29375.00 52096.00 24999.00 13693.00
37.5 11.1 34896.00 65357.00 81818.00 20586.00
34 12.6 20578.00 113239.00 24999.00 14095.00
28.7 12.1 32574.00 50244.00 49662.00 14393.00
36.1 12.2 30589.00 69375.00 48890.00 16352.00
30.6 12.3 26565.00 64038.00 42543.00 17410.00
22.8 12.3 16590.00 67850.00 24999.00 10436.00
30.3 12.2 9354.00 91708.00 24999.00 9904.00
22 12 14115.00 53923.00 24999.00 9071.00
30.8 11.9 17992.00 46885.00 24999.00 10679.00
35.1 11 7741.00 99375.00 24999.00 6207.00
Region Type Price Cost
S C 348744 53000.00
S C 274455 41000.00
S C 277720 44650.00
S C 307373 41292.00
S C 271105 45000.00
S C 262740 44900.00
S C 175000 28000.00
S C 201700 40940.00
S C 283440 50900.00
S C 185160 29000.00
S C 323716 34500.00
S C 281487 57285.00
S C 184460 22300.00
S C 289000 44000.00
S C 410810 66500.00
S C 184210 28000.00
S C 223890 28000.00
S C 189120 35000.00
S C 230440 33000.00
S C 330486 35000.00
S C 250005 33000.00
S C 203950 33000.00
S C 230555 28000.00
S C 183370 28000.00
S R 112740 20700.00
S R 179365 32200.00
S R 155870 24650.00
S R 155270 19600.00
S R 116415 19600.00
S R 147905 24650.00
S R 139955 30400.00
S R 184873 33400.00
S R 212079 33400.00
S R 265500 35800.00
S R 175470 28600.00
S R 115350 18030.00
S R 85145 17030.00
S R 139435 29155.00
S R 133070 24455.00
S C 165220 25500.00
S C 136530 25500.00
S C 153845 27500.00
S C 165350 25000.00
S C 168354 27316.00
S C 170000 25200.00
S C 210380 33856.00
S C 268210 29700.00
S C 233900 44200.00
S C 168500 33000.00
S C 248500 20000.00
S C 220257 31300.00
S C 214900 31300.00
S C 211513 31300.00
S C 188603 31300.00
S R 187390 27000.00
MW R 335000 68375.00
MW R 294450 73400.00
MW R 267060 73400.00
MW R 250800 73400.00
MW R 269410 73400.00
MW R 267640 73400.00
MW R 260100 73400.00
MW C 301500 59000.00
MW C 309075 82250.00
MW C 290190 82250.00
MW C 322920 82250.00
MW C 319602 82250.00
MW C 294990 57000.00
MW C 286758 57000.00
MW C 352781 60000.00
MW C 310372 60000.00
MW C 400330 75510.00
MW C 446507 75510.00
MW R 198202 45025.00
MW R 200423 45025.00
MW R 181916 45025.00
MW R 203076 45025.00
MW R 196898 45025.00
MW R 182237 45025.00
MW R 224108 45025.00
MW R 230000 45025.00
MW R 172749 45025.00
MW C 318274 85800.00
MW C 191028 45000.00
MW C 200119 45000.00
MW C 242899 48252.00
MW C 387527 48000.00
MW C 257040 37631.00
MW C 270518 46499.00
MW C 265058 41404.00
MW C 255000 43198.00
MW C 385942 49123.00
MW C 354065 48115.00
MW C 333158 49123.00
MW C 254048 39680.00
MW C 246648 41600.00
MW C 367600 50000.00
MW C 318523 50000.00
MW C 359949 50591.00
MW C 281824 50448.00
MW C 355688 65373.00
MW C 305000 49067.00
MW C 299096 43784.00
MW C 280622 45130.00
MW C 404510 58225.00
MW C 371152 58223.00
MW C 219990 37557.00
MW C 432426 57422.00
MW C 268000 43344.00
MW C 312898 40768.00
MW C 267250 45676.00
MW C 379000 72915.00
MW C 342423 48309.00
MW C 337374 70399.00
MW C 358162 44470.00
MW C 398651 65429.00
MW C 280804 40667.00
MW C 407076 48668.00
MW C 268500 41099.00
MW C 444304 53938.00
MW C 324266 47891.00
MW C 307387 45850.00
MW C 369101 46773.00
MW C 350702 46386.00
MW C 329611 48611.00
MW C 242191 33434.00
MW C 379424 64902.00
MW C 324412 62523.00
MW C 340730 50850.00
MW C 310100 41800.00
MW C 354117 56219.00
MW C 330710 49920.00
MW C 417790 63099.00
MW C 290000 48300.00
MW C 274903 45345.00
MW C 209400 43579.00
MW C 205821 39299.00
MW C 287771 46300.00
MW C 575120 79790.00
MW C 226000 35600.00
MW C 216049 35600.00
MW C 207345 35600.00
MW C 211797 34000.00
MW C 204900 34000.00
MW C 206400 35851.00
MW C 186000 35851.00
MW C 249900 38200.00
MW C 214205 36500.00
MW C 256235 48500.00
MW C 262890 48500.00
MW C 338065 54850.00
MW C 326570 51000.00
MW C 239000 39169.00
MW C 239870 41354.00
MW C 241195 41340.00
MW C 252135 41341.00
MW C 253055 41340.00
MW C 160000 29500.00
MW C 337380 49150.00
MW C 492820 84122.00
MW C 385000 75000.00
MW C 340000 40000.00
MW C 202000 31160.00
MW C 234971 29202.00
MW C 225900 28618.00
MW C 366990 55508.00
MW C 307663 44840.00
MW C 379575 44294.00
Name,PrizeMoney,DrivingAccuracy ,GIR,PuttingAverage,BirdieConversion,PuttsPerRound
Aaron Baddeley,60661,60.73,58.26,1.745,31.36,27.96
Adam Scott,262045,62,69.12,1.767,30.39,29.28
Alex Aragon,3635,51.12,59.11,1.787,29.89,29.2
Alex Cejka,17516,66.4,67.7,1.777,29.33,29.46
Arjun Atwal,16683,63.24,64.04,1.761,29.32,28.93
Arron Oberholser,107294,62.53,69.27,1.775,29.2,29.56
Bart Bryant,50620,72.76,68.67,1.812,24.95,30.06
Ben Crane,57273,63.51,62.01,1.736,32.28,28.46
Ben Curtis,86782,66.61,65.25,1.798,26.36,29.5
Bernhard Langer,23396,62.41,65.66,1.778,28.94,29.17
Bill Haas,29567,57.71,64.24,1.786,29.39,29.04
Billy Andrade,44080,62.84,66.44,1.788,27.7,29.31
Billy Mayfair,47172,70.14,66.55,1.796,26.65,29.36
Bo Van Pelt,49640,60.72,66.45,1.772,31.09,29.24
Bob Estes,53610,64.01,66.73,1.777,28.07,29.16
Bob May,26129,61.9,69.09,1.785,27.86,29.47
Bob Tway,11989,61.16,64.17,1.771,27.68,29.08
Brad Faxon,20911,57.56,58.05,1.771,28.13,28.5
Brandt Jobe,28658,61.12,67.23,1.79,28.02,29.59
Brent Geiberger,19683,67.04,64.95,1.778,28.04,28.96
Brett Quigley,79316,58.85,67.83,1.779,29.04,29.19
Brett Wetterich,120927,61.46,67.82,1.766,32.23,29.3
Brian Bateman,24814,59.78,64.53,1.808,25.53,29.29
Brian Davis,27224,70.06,65.37,1.79,28.34,29.21
Brian Gay,33471,68.64,63.19,1.727,31.05,28.05
Briny Baird,33782,71.39,70.2,1.777,28.87,29.64
Bubba Dickerson,20322,62.81,65.63,1.777,29.89,29.32
Bubba Watson,37751,51.5,66.74,1.797,30.66,29.56
Camilo Villegas,60073,58.06,65.29,1.78,30.55,29.29
Carl Pettersson,94571,61.01,62.31,1.776,29.62,28.84
Carlos Franco,15668,55.71,65.18,1.814,27.98,29.9
Chad Campbell,112443,59.77,67.43,1.795,28.02,29.51
Charles Howell III,51770,56.39,65.89,1.823,26.23,29.75
Charles Warren,37735,64.09,67.96,1.797,27.51,29.41
Charley Hoffman,38455,58.01,66.61,1.753,31.94,28.94
Chris Couch,50249,60.98,66.15,1.793,30.53,29.83
Chris DiMarco,59151,64.34,66.45,1.772,29.22,29.22
Chris Riley,18345,70.55,62.47,1.755,30.03,28.61
Chris Smith,8734,66.02,69.32,1.806,27.01,30.18
Corey Pavin,56873,67.66,63.57,1.815,23.43,28.91
Craig Barlow,45752,58.04,65.75,1.769,30.54,29.41
D.A. Points,14499,62.37,62.92,1.773,30.35,28.95
D.J. Trahan,31371,64.04,64.01,1.78,29.03,29.26
Daisuke Maruyama,38275,71.75,66.24,1.783,28.5,29.23
Daniel Chopra,46377,56.77,63.5,1.712,34.75,28.21
Danny Ellis,16630,60.58,62.59,1.764,28.93,28.84
Darron Stiles,10504,68.23,69.67,1.789,27.17,29.67
David Duval,13262,51.09,60.06,1.767,30.91,28.87
David Howell,65174,62.38,63.21,1.73,32.88,28.89
David McKenzie,15187,67.41,65.83,1.777,28.69,29.19
David Toms,132327,69.18,66.18,1.759,31.43,29.01
Davis Love III,119444,59.89,66.59,1.751,31.53,29
Dean Wilson,73819,63.47,65.27,1.765,29.26,28.83
Doug Barron,13865,67.49,60.43,1.781,28.59,28.58
Dudley Hart,26301,58.13,63.79,1.783,28.99,28.92
Duffy Waldorf,22340,67.92,66.24,1.803,28.35,29.47
Eric Axley,43951,63.68,65.73,1.748,31.14,28.8
Ernie Els,129234,57.62,63.72,1.759,30.51,28.72
Frank Lickliter II,57092,66.62,67.98,1.763,29.87,29.04
Fred Couples,45904,54.36,65.33,1.816,27.01,30.02
Fred Funk,54477,78.01,66.35,1.785,27.1,29.33
Fredrik Jacobson,43820,62.45,63.99,1.76,29.61,28.79
Geoff Ogilvy,217748,61.89,62.94,1.769,31.44,28.93
Greg Chalmers,5402,53.9,59.04,1.764,29.06,28.63
Greg Kraft,10528,70.01,62.74,1.77,27.22,28.95
Greg Owen,54862,67.13,68.54,1.812,28.87,29.64
Harrison Frazar,30656,60.94,64.04,1.748,32.7,28.55
Heath Slocum,39356,74.67,67.8,1.792,27.84,29.58
Henrik Bjornstad,15840,62.55,64.39,1.8,27.3,29.32
Hidemichi Tanaka,2240,58.88,57.91,1.824,23.92,29.26
Hunter Mahan,38188,68.31,67.4,1.777,31.06,29.65
Ian Leggatt,13031,61.1,63.23,1.793,27.93,29.28
Ian Poulter,103594,70.19,66.03,1.772,28.8,29.06
J.B. Holmes,57216,54.13,65.3,1.802,31.56,29.75
J.J. Henry,82196,60.03,68.16,1.788,28.12,29.69
J.L. Lewis,25804,64.2,62.67,1.802,26.37,29.23
J.P. Hayes,36918,66.88,64.24,1.753,32.6,28.84
James Driscoll,7583,50.54,61.32,1.817,28.83,29.39
Jason Bohn,57824,66.78,65.87,1.797,27.89,29.18
Jason Gore,24724,59.95,63.03,1.796,29.97,29.5
Jason Schultz,5265,59.43,63.19,1.808,27.73,29.45
Jeff Brehaut,16927,66.17,67.3,1.787,29.35,29.67
Jeff Gove,27361,65.65,72.03,1.813,25.8,30.11
Jeff Maggert,55014,71.86,65.36,1.789,27.5,29.33
Jeff Overton,20612,58.28,65.29,1.793,28.41,29.18
Jeff Sluman,43173,64.81,65.24,1.763,29.54,29.05
Jerry Kelly,56058,70.26,67.11,1.781,28.46,29.17
Jerry Smith,19594,69.66,66.55,1.78,28.79,29.35
Jesper Parnevik,54513,61.37,64.42,1.745,30.9,28.58
Jim Furyk,300555,73.85,70.71,1.742,30.47,28.85
Jimmy Walker,7331,49.75,61.97,1.764,28.84,29.03
Joe Durant,100398,78.43,69.75,1.785,28.67,29.81
Joe Ogilvie,37004,64.07,65.08,1.757,30.99,28.7
Joey Sindelar,27673,65.58,68.64,1.799,27.09,30
John Cook,29296,70.65,66.33,1.789,27.07,29.31
John Daly,9149,52.48,60.88,1.79,31.47,29.37
“John Engler, Jr.”,2692,58.07,59.78,1.809,25.28,29.36
John Huston,15964,58.74,62.58,1.777,30.24,28.92
John Rollins,53530,64.22,66.12,1.755,30.73,29.15
John Senden,58953,64.95,71.15,1.793,27.23,29.84
Jon Mills,2426,57.05,62.71,1.835,24.36,29.65
Jonathan Byrd,70421,64.15,69.61,1.75,33.09,29.06
Jonathan Kaye,18085,65.37,68.42,1.828,25.8,29.99
Jose Maria Olazabal,117801,59.68,65.52,1.77,28.89,29.1
Justin Leonard,30068,67.24,63.36,1.779,26.34,28.87
Justin Rose,58189,64,67.89,1.759,31.72,29
K.J. Choi,91406,65.03,68.12,1.792,27.53,29.51
Kenny Perry,37214,68.45,68.75,1.813,26.46,30.19
Kent Jones,26899,67.95,65.46,1.762,29.54,29.2
Kevin Sutherland,25918,64.52,68.29,1.796,27.15,29.5
Kirk Triplett,42589,72.76,65.28,1.757,29.05,28.79
Kris Cox,18494,59.31,66.39,1.783,28.66,29.3
Larry Mize,12110,74.33,64.16,1.803,23.17,29.18
Lee Janzen,18721,56.39,63.08,1.765,30.58,29.23
Len Mattiace,3025,56.71,58.85,1.773,29.21,28.75
Lucas Glover,83483,63.69,67.33,1.763,32.02,29.15
Luke Donald,176523,66.86,66.05,1.752,30.19,28.54
Marco Dawson,20188,61.17,64.94,1.752,30.74,28.67
Mark Brooks,5777,69.35,62.92,1.799,23.86,29.27
Mark Calcavecchia,26123,65.3,62.07,1.792,30.67,29.37
Mark O’Meara,11315,57.35,59.44,1.778,27.66,28.7
Mark Wilson,18513,67.23,67.98,1.758,29.85,29.28
Mathew Goggin,41390,63.5,67.14,1.824,27.78,30.16
Mathias Gronberg,22467,62.51,66.54,1.8,29.17,29.39
Matt Hansen,7490,59.85,66.92,1.839,27.36,30.15
Michael Allen,18838,58.01,62.7,1.746,31.86,28.48
Michael Connell,4444,60.99,64.12,1.819,26.4,30
Mike Sposa,5285,65.71,63.16,1.823,26.93,29.37
Mike Weir,78489,64.1,66.47,1.752,30.73,29.06
Nathan Green,56693,63.1,63.03,1.757,31.32,28.56
Nicholas Thompson,8272,67.44,65.35,1.829,27.17,30.12
Nick Watney,42890,61.78,65.38,1.788,31.72,29.08
Olin Browne,25135,73.56,64.32,1.764,29.06,28.86
Omar Uresti,26532,74.3,66.38,1.807,25.89,29.34
Padraig Harrington,89312,66.14,65.22,1.758,31.18,29.04
Pat Perez,37869,59.31,64.67,1.771,30.05,29.04
Patrick Sheehan,11376,64.99,63.89,1.788,26.92,28.99
Paul Azinger,23403,62.65,62.39,1.769,28,28.38
Paul Goydos,37100,72.9,65.28,1.793,26.98,29.17
Paul Stankowski,14527,61.23,65.87,1.811,26.9,29.71
Peter Lonard,38046,64.19,63.16,1.824,25.35,29.51
Phil Mickelson,224027,58.61,68.28,1.731,35.66,28.91
Retief Goosen,145414,57.28,65.46,1.771,30.26,28.98
Rich Beem,24379,63.24,66.2,1.787,29.25,29.6
Richard S. Johnson,53634,67.99,66.02,1.764,28.43,28.94
Robert Allenby,68345,69.33,67.89,1.746,31.09,29.1
Robert Damron,14558,62.93,63.55,1.781,27.87,28.93
Robert Gamez,16455,69.51,64.83,1.788,27.67,29.26
Robert Garrigus,19200,57.25,64.66,1.792,31.43,29.28
Rod Pampling,111028,60.46,62.5,1.745,30.65,28.44
Roger Tambellini,4667,57.53,65.39,1.833,26.43,30.18
Ron Whittaker,10715,68.57,66.03,1.792,26.2,29.63
Rory Sabbatini,119240,56.49,64.52,1.781,30.18,28.92
Ryan Hietala,3816,51.03,60.08,1.809,26.96,29.36
Ryan Moore,51005,66.96,63.25,1.773,30.27,28.69
Ryan Palmer,36428,62.03,64.2,1.764,30.93,28.8
Ryuji Imada,32843,58.62,64.81,1.764,28.04,28.87
Scott Gutschewski,19973,64.98,66.67,1.77,29.06,29.53
Scott Verplank,69173,75.23,66.02,1.755,29.9,28.72
Sean O’Hair,47046,63.7,64.74,1.8,29.74,29.56
Sergio Garcia,91808,61.11,67.47,1.802,30.43,29.78
Shane Bertsch,20502,67.35,63.89,1.751,30.28,28.71
Shaun Micheel,56305,61.79,67.62,1.777,28.05,29.53
Shigeki Maruyama,38471,61.37,63.59,1.749,30.86,28.58
Skip Kendall,19997,72.08,67.21,1.77,27.24,29.15
Stephen Ames,114055,62.98,66.49,1.752,30.41,29.05
Stephen Leaney,27657,65.81,67.13,1.755,27.88,28.95
Steve Elkington,15012,73.91,69.61,1.817,24.96,29.88
Steve Flesch,42958,65.59,65.99,1.771,28.43,28.88
Steve Jones,11421,64.51,62.8,1.809,26.24,29.33
Steve Lowery,36289,62.52,65.46,1.782,28.33,29.14
Steve Stricker,106577,66.98,68.01,1.734,30.56,28.26
Stewart Cink,105997,59.72,64.9,1.756,30.99,28.75
Stuart Appleby,150889,59.62,63.53,1.743,31.53,28.63
Tag Ridings,15098,54.1,61.22,1.766,31.72,28.79
Ted Purdy,36861,65.86,67.05,1.796,28.62,29.66
Thomas Levet,9062,64.01,65.9,1.851,23.71,30.1
Tiger Woods,662771,60.71,74.15,1.756,35.26,29.38
Tim Clark,89770,67.27,65.96,1.763,30.66,29.03
Tim Herron,65783,58.28,62.62,1.802,27.17,29.44
Tim Petrovic,20064,63.54,61.29,1.799,27.35,29.01
Tjaart van der Walt,11187,69.69,64.52,1.808,25.45,29.42
Todd Fischer,11309,64.83,62.23,1.744,29.27,28.59
Todd Hamilton,6117,56.32,56.87,1.792,27.38,29.01
Tom Lehman,84604,60.96,65.93,1.827,24.37,30.08
“Tom Pernice, Jr.”,72623,65.28,64.53,1.756,31.7,28.88
Tommy Armour III,14098,66.74,63.76,1.752,29.97,29.08
Trevor Immelman,160175,62.08,69.06,1.771,30.06,29.26
Troy Matteson,55581,59.45,66.34,1.803,28.12,29.52
Vance Veazey,10354,57.58,61.58,1.752,30.02,28.93
Vaughn Taylor,68613,63.31,64.08,1.725,34.26,28.67
Vijay Singh,170460,59.4,67.83,1.753,31.77,28.89
“Wes Short, Jr.”,12803,57.78,64.18,1.782,30.56,29.7
Will MacKenzie,30344,62.91,67.99,1.809,28.65,29.97
Woody Austin,38043,61.11,62.67,1.773,30.49,28.72
Zach Johnson,90824,69.63,66.86,1.774,26.94,29.32