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Строим модели с новыми зависимыми переменными

. . reg priceperm_star totsp kitsp dist metrdist walk brick tel floor new floors nfloor sw

Source

SS

df MS

Number of obs

= 763

F( 12, 750)

= 59.91

Model

9.59000974

12 .799167479

Prob > F

= 0.0000

Residual

10.004223

750 .013338964

R-squared

= 0.4894

Adj R-squared

= 0.4813

Total

19.5942328

762 .025714216

Root MSE

= .11549

priceperm_~r

Coef.

Std. Err. t

P>t

[95% Conf.

Interval]

totsp

-.0040999

.0009803 -4.18

0.000

-.0060243

-.0021754

kitsp

.0216728

.0040109 5.40

0.000

.0137988

.0295467

dist

-.0209141

.0012389 -16.88

0.000

-.0233462

-.0184821

metrdist

-.0058656

.0010097 -5.81

0.000

-.0078478

-.0038835

walk

.0948849

.0092563 10.25

0.000

.0767136

.1130561

brick

.0451822

.0103083 4.38

0.000

.0249456

.0654187

tel

.0242942

.0119652 2.03

0.043

.0008051

.0477834

floor

.0724951

.0101346 7.15

0.000

.0525995

.0923907

new

-.0747356

.0288712 -2.59

0.010

-.1314136

-.0180575

floors

.007405

.001156 6.41

0.000

.0051356

.0096743

nfloor

.0047846

.0010727 4.46

0.000

.0026789

.0068904

sw

.0433601

.0088736 4.89

0.000

.02594

.0607802

_cons

1.063269

.0377433 28.17

0.000

.9891742

1.137364

. reg lgpriceperm_star lntotsp lnkitsp lndist lnmetrdist lnfloors lnnfloor walk brick tel floor new sw

Source

SS

df MS

Number of obs

= 763

F( 12, 750)

= 58.59

Model

9.112745

12 .759395416

Prob > F

= 0.0000

Residual

9.72142331

750 .012961898

R-squared

= 0.4838

Adj R-squared

= 0.4756

Total

18.8341683

762 .024716756

Root MSE

= .11385

lgpriceper~r

Coef.

Std. Err. t

P>t

[95% Conf.

Interval]

lntotsp

-.1994132

.0491942 -4.05

0.000

-.2959879

-.1028385

lnkitsp

.1488611

.0338481 4.40

0.000

.0824127

.2153094

lndist

-.2114004

.0131866 -16.03

0.000

-.2372874

-.1855133

lnmetrdist

-.0388459

.008135 -4.78

0.000

-.0548159

-.022876

lnfloors

.0979816

.0127182 7.70

0.000

.0730141

.122949

lnnfloor

.0251988

.005752 4.38

0.000

.0139068

.0364908

walk

.0992604

.0090582 10.96

0.000

.0814781

.1170428

brick

.055766

.0103708 5.38

0.000

.0354067

.0761253

tel

.0268293

.0117936 2.27

0.023

.0036769

.0499816

floor

.0569536

.0105399 5.40

0.000

.0362624

.0776448

new

-.0656276

.0283623 -2.31

0.021

-.1213064

-.0099487

sw

.0445313

.0087298 5.10

0.000

.0273935

.0616691

_cons

.6331904

.1493441 4.24

0.000

.3400082

.9263725

Сравниваем RSS в обоих моделях:

Χ2 = n/2 * ln(RSSmax/RSSmin) ~ Χ21

Χ2 = 763/2 * ln(10/9,7) ~ Χ21

11,62 ~ Χ21

0,00065. На 1% уровне значимости больше подходит логарифмическая модель.

Сравнивая R2adj в логарифмической и полулогарифмической моделях, а также Root MSE в обоих моделях, приходим к выводу о том, что полулогарифмическая модель обладает самой высокой описательной способностью.

. reg lnpriceperm totsp kitsp dist metrdist walk brick tel floor new floors nfloor sw

Source

SS

df MS

Number of obs

= 763

F( 12, 750)

= 61.48

Model

9.33934623

12 .778278852

Prob > F

= 0.0000

Residual

9.49482307

750 .012659764

R-squared

= 0.4959

Adj R-squared

= 0.4878

Total

18.8341693

762 .024716758

Root MSE

= .11252

lnpriceperm

Coef.

Std. Err. t

P>t

[95% Conf.

Interval]

totsp

-.0039887

.000955 -4.18

0.000

-.0058635

-.002114

kitsp

.0208337

.0039075 5.33

0.000

.0131629

.0285046

dist

-.0202985

.0012069 -16.82

0.000

-.0226679

-.0179292

metrdist

-.0064124

.0009837 -6.52

0.000

-.0083435

-.0044814

walk

.0943859

.0090175 10.47

0.000

.0766833

.1120885

brick

.0437063

.0100424 4.35

0.000

.0239917

.0634209

tel

.0226295

.0116565 1.94

0.053

-.0002538

.0455129

floor

.0747079

.0098732 7.57

0.000

.0553255

.0940904

new

-.0714038

.0281266 -2.54

0.011

-.12662

-.0161876

floors

.007333

.0011262 6.51

0.000

.0051222

.0095438

nfloor

.0043739

.001045 4.19

0.000

.0023225

.0064254

sw

.0427449

.0086448 4.94

0.000

.0257741

.0597157

_cons

8.648569

.0367698 235.21

0.000

8.576385

8.720753