The REG Procedure Model: MODEL1 Dependent Variable: totsat Number of Observation

Question

The REG Procedure

Model: MODEL1

Dependent Variable: totsat

Number of Observations Read

51

Number of Observations Used

51

Analysis of Variance

Source

DF

Sum of
Squares

Mean
Square

F Value

Pr > F

Model

1

41712

41712

5.18

0.0272

Error

49

394455

8050.10685

Corrected Total

50

436167

Root MSE

89.72239

R-Square

0.0956

Dependent Mean

1070.05882

Adj R-Sq

0.0772

Coeff Var

8.38481

Parameter Estimates

Variable

Label

DF

Parameter
Estimate

Standard
Error

t Value

Pr > |t|

Intercept

Intercept

1

1175.62307

48.04726

24.47

<.0001

texppp

texppp

1

-0.00953

0.00419

-2.28

0.0272

The REG Procedure

Model: MODEL2

Dependent Variable: totsat

Number of Observations Read

51

Number of Observations Used

51

Analysis of Variance

Source

DF

Sum of
Squares

Mean
Square

F Value

Pr > F

Model

2

365843

182922

124.85

<.0001

Error

48

70324

1465.07836

Corrected Total

50

436167

Root MSE

38.27634

R-Square

0.8388

Dependent Mean

1070.05882

Adj R-Sq

0.8321

Coeff Var

3.57703

Parameter Estimates

Variable

Label

DF

Parameter
Estimate

Standard
Error

t Value

Pr > |t|

Intercept

Intercept

1

1139.03000

20.64449

55.17

<.0001

texppp

texppp

1

0.00307

0.00198

1.55

0.1270

pertake

pertake

1

-2.51564

0.16913

-14.87

<.0001

The REG Procedure

Model: MODEL3

Dependent Variable: totsat

Number of Observations Read

51

Number of Observations Used

51

Analysis of Variance

Source

DF

Sum of
Squares

Mean
Square

F Value

Pr > F

Model

3

374348

124783

94.87

<.0001

Error

47

61818

1315.28406

Corrected Total

50

436167

Root MSE

36.26685

R-Square

0.8583

Dependent Mean

1070.05882

Adj R-Sq

0.8492

Coeff Var

3.38924

Parameter Estimates

Variable

Label

DF

Parameter
Estimate

Standard
Error

t Value

Pr > |t|

Intercept

Intercept

1

1125.67840

20.25306

55.58

<.0001

iexppp

iexppp

1

-0.01388

0.00692

-2.00

0.0508

nexppp

1

0.01432

0.00480

2.98

0.0045

pertake

pertake

1

-2.52324

0.16028

-15.74

<.0001

The REG Procedure

Model: MODEL4

Dependent Variable: totsat

Number of Observations Read

51

Number of Observations Used

51

Analysis of Variance

Source

DF

Sum of
Squares

Mean
Square

F Value

Pr > F

Model

3

374624

124875

95.37

<.0001

Error

47

61543

1309.42363

Corrected Total

50

436167

Root MSE

36.18596

R-Square

0.8589

Dependent Mean

1070.05882

Adj R-Sq

0.8499

Coeff Var

3.38168

Parameter Estimates

Variable

Label

DF

Parameter
Estimate

Standard
Error

t Value

Pr > |t|

Intercept

Intercept

1

670.63509

197.97940

3.39

0.0014

liexppp

1

-69.78365

36.23337

-1.93

0.0602

lnexppp

1

121.84843

41.88364

2.91

0.0055

pertake

pertake

1

-2.52523

0.15776

-16.01

<.0001

Present your results for model 4 in equation form. Is this model preferred over model 3? EXPALIN? How should you interpret the coefficient on the two logged expenditure variables? DEMONSTRATE!

OF all the models which one do you prefer and why?

Number of Observations Read

51

Number of Observations Used

51

Explanation / Answer

Model 4:

totsat=670.635 -69.78*liexppp + 121.84 *lnexppp -2.525 *pertake

Yes this model has more adjusted R-sq.

interpretions:

for unit increase in log of exppp gives rise of 121 to totsat. or unit increase in exppp increases totsat by

3.55E+52 units

Model 4 is most preferred since it has highest adj-R-sq and all the variables are significant at 6% level.

3.55E+52 units

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