buat SAS...

data gpadata;
input gpa test;
cards;
3.1 5.5
2.3 4.8
3.0 4.7
1.9 3.9
2.5 4.5
3.7 6.2
3.4 6.0
2.6 5.2
2.8 4.7
1.6 4.3
2.0 4.9
2.9 5.4
2.3 5.0
3.0 6.3
1.8 4.6
1.4 4.3
2.0 5.0
3.8 5.9
2.2 4.1
1.5 4.7
;
proc reg data = gpadata;
model gpa = test / xpx i covb corrb;
run;


OUTPUT:
The SAS System 08:39 Thursday, August 13, 2009 1

The REG Procedure
Model: MODEL1

Model Crossproducts X'X X'Y Y'Y

Variable Intercept test gpa

Intercept 20 100 49.8
test 100 509.12 256.4
gpa 49.8 256.4 133.6

The SAS System 08:39 Thursday, August 13, 2009 2

The REG Procedure
Model: MODEL1
Dependent Variable: gpa

Number of Observations Read 20
Number of Observations Used 20


X'X Inverse, Parameter Estimates, and SSE

Variable Intercept test gpa

Intercept 2.7912280702 -0.548245614 -1.567017544
test -0.548245614 0.1096491228 0.8114035088
gpa -1.567017544 0.8114035088 3.5936140351


Analysis of Variance

Sum of Mean
Source DF Squares Square F Value Pr > F

Model 1 6.00439 6.00439 30.08 <.0001
Error 18 3.59361 0.19965
Corrected Total 19 9.59800


Root MSE 0.44682 R-Square 0.6256
Dependent Mean 2.49000 Adj R-Sq 0.6048
Coeff Var 17.94445


Parameter Estimates

Parameter Standard
Variable DF Estimate Error t Value Pr > |t|

Intercept 1 -1.56702 0.74650 -2.10 0.0502
test 1 0.81140 0.14796 5.48 <.0001


Covariance of Estimates

Variable Intercept test

Intercept 0.5572553538 -0.109454619
test -0.109454619 0.0218909237



The SAS System 08:39 Thursday, August 13, 2009 3

The REG Procedure
Model: MODEL1
Dependent Variable: gpa

Correlation of Estimates

Variable Intercept test

Intercept 1.0000 -0.9910
test -0.9910 1.0000


menarik x? menarik x? ngahaha... srownok jgk wat bnder alah neyh...
p/s: ku buat try buat n gne SAS applictn neyh mse klas regression... thanx to mrs.mazni... ngehehe =p