The name is absent



Stata Technical Bulletin

29


. regress job fem phd ment fel art cit

Source I

SS

df

MS

Number of obs =     408

T? /  Л    ЛЛП —   *7 70

1 ∖   Vt     *±V ɪ √

—      -L I . I Q

Model I

81.0584763

6 13.

5097461

Prob > F

= 0.0000

Residual ∣

304.737915

401 .759944926

R-Squared

= 0.2101

Adj R-Squared

= 0.1983

Total I

385.796392

407 .947902683

Root MSE

= .87175

job I

Coef.

Std. Err.

t

p>∣t∣

[957. Conf.

Interval]

---------+-

fem I

-.1391939

.0902344

-1.543

0.124

-.3165856

.0381977

phd I

.2726826

.0493183

5.529

0.000

.1757278

.3696375

ment I

.0011867

.0007012

1.692

0.091

-.0001917

.0025651

fel I

.2341384

.0948206

2.469

0.014

.0477308

.4205461

art I

.0228011

.0288843

0.789

0.430

-.0339824

.0795846

cit I

.0044788

.0019687

2.275

0.023

.0006087

.008349

_cons I

1.067184

.1661357

6.424

0.000

.7405785

1.39379

Iistcoef provides additional information:

. Iistcoef, help std constant

regress (N=408): Unstandardized and Standardized Estimates
Observed SD: .97360294
SD of Error: .8717482

job I

b

t

p>∣t∣

bStdX

bStdY

bStdXY

SDofX

fem I

-0.13919

-1.543

0.124

-0.0680

-0.1430

-0.0698

0.4883

phd I

0.27268

5.529

0.000

0.2601

0.2801

0.2671

0.9538

ment I

0.00119

1.692

0.091

0.0778

0.0012

0.0799

65.5299

fel I

0.23414

2.469

0.014

0.1139

0.2405

0.1170

0.4866

art I

0.02280

0.789

0.430

0.0514

0.0234

0.0528

2.2561

cit I

0.00448

2.275

0.023

0.1481

0.0046

0.1521

33.0599

„cons I

1.06718

6.424

0.000

b =

raw coefficient

t =

t-score for

test of

b=0

p>∣t∣ =

p-value for

t-test

bStdX = х-standardized coefficient
bStdY = у-standardized coefficient

bStdXY = fully standardized coefficient

SDofX = standard deviation of X

Example with logit

The logit model illustrates that Iistcoef can be used to obtain alternative transformations of the basic parameters. We
begin by estimating the logit model, which produces the standard output:

. logit Ifp k5 k618 age we he Iwg inct nolog

Logit estimates

Log likelihood = -452.63296

Number of obs =
LR chi2(7)

Prob > chi2     =

Pseudo R2       =

753
124.48
0.0000
0.1209

Ifp I

Coef.

Std. Err.

z

P>∣z∣

[957. Conf.

Interval]

кБ I

-1.462913

. 1970006

-7.426

0.000

-1.849027

-1.076799

k618 I

-.0645707

.0680008

-0.950

0.342

-.1978499

.0687085

age I

-.0628706

.0127831

-4.918

0.000

-.0879249

-.0378162

wc I

.8072738

.2299799

3.510

0.000

.3565215

1.258026

he I

.1117336

.2060397

0.542

0.588

-.2920969

.515564

Iwg I

.6046931

.1508176

4.009

0.000

.3090961

.9002901

inc I

-.0344464

.0082084

-4.196

0.000

-.0505346

-.0183583

_cons I

3.18214

.6443751

4.938

0.000

1.919188

4.445092

Most frequently, the logit model is interpreted using factor change coefficients, also known as odds ratios. These are the default
option for Iistcoef.

. Iistcoef, help



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