4. The following output is taken from applying Backward elimination variable selection

procedure to fit a regression model to predict Y (Earnings) using Scoring Avg., Greens in

Reg., Putting Avg. and Sand Saves. (consider to remove = 0.05)

Regression Analysis: Earnings Scoring Avg., Greens in Reg., Putting Avg. and Sand Saves

Candidate terms: Scoring Avg., Greens in Reg., Putting Avg., Sand Saves

----Step 1---- -----Step 2---- Coef P Coef P Coef P

-----Step 3----

31737 -440.3 0.000

1507 0.022

 262.749 64.57% 61.94% 39.06% 

5.78

Constant 19835 ScoringAvg. -248

0.050 0.056 0.211 0.007 

22252 -344.9 0.001

 3726 0.092 1622 0.012 

 253.259 68.30% 64.64% 44.93% 

4.64

Greens in Reg. Putting Avg. Sand Saves 

 4326 -2795 1767 

S 250.178 R-sq 70.26% R-sq(adj) 65.50% R-sq(pred) 40.91%Mallows Cp 5.00

1. a) What variables should be included in the model from the above variable selection procedure?

2. b) According to this output, write down the estimated regression equation to predict Earnings.

The following output is taken from applying Best Subsets Regression to fit a regression model to

predict RPG (runs/game) statistic.

Best Subsets Regression: Earnings vs Scoring Avg., Greens in Reg., Putting Avg. and Sand Saves

Response is Earnings ($1000)

R-Sq Vars R-Sq (adj) 1 56.8 55.3 1 32.0 29.6 2 64.6 61.9 2 59.5 56.5

R-Sq Mallows (pred) Cp 28.1 10.3 0.4 31.1 39.1 5.8 29.8 10.1

G

r SeP ceu

ontS rsta iin nind gng

S ARAa vevv ggge S ...s

284.75 X 357.41 X 262.75 X X 281.05 X X 

3 68.364.644.9 3 65.561.532.6 4 70.3 65.5 40.9

4.6253.26XXX 7.0264.35XXX 5.0 250.18 XXXX

c) Using R-Square adjusted as the criteria, what variables should be included in the best two- variable estimated regression equation?