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Приложение 11

Значимые коэффициенты корреляции с переменными «Принадлежность к кластеру»

Переменная «Принадлежность к кластеру»

Переменная «Фактор»

Хи-квадрат Пирсона (уровень значимости)

Кластер 1

Возраст компании (s6v15)

9,214 (0,027)

Доля временно занятых сотрудников (s2v6h)

14,712 (0,005)

Кластер 2

Разбивка на отрасли (s6v1_industry)

20,300 (0,016)

Предоставление дополнительного пенсионного обеспечения (s4v3g)

6,710 (0,01)

Наличие и распространение плановых ротаций

(s3v8i)

12,623 (0,013)

Возраст компании (s6v15)

13,315 (0,004)

доля сотрудников-членов профсоюза (s5v1)

16,721 (0,002)

Доля частично занятых сотрудников (s2v6e)

8,908 (0,031)

Доля сотрудников, работающих по сменам (s2v6b)

14,524 (0,006)

Количество работников (s1v1)

23,567 (0)

Доля сотрудников с высшим образованием (s6v13)

14,606 (0,006)

Доля специалистов, по сравнению со служащими (s1v2b_c)

6,825 (0,009)

Доля сотрудников 25 лет и младше (s6v11)

28,569 (0)

Доля сотрудников 50 лет и старше (s6v12)

15,631 (0,008)

Кластер 3

Разбивка на отрасли (s6v1_industry)

17,311 (0,044)

Возраст компании (s6v15)

11,498 (0,009)

Кластер 4

доля сотрудников-членов профсоюза (s5v1)

9,983 (0,041)

доля женщин (s1v1_woman)

9,246 (0,026)

Кластер 5

Использование обучения на рабочем месте (s3v8c)

12,037 (0,017)

Состояние рынка, на котором осуществляет деятельность компания (s6v7_recoded)

10,314 (0,006)

Разбивка на отрасли (s6v1_industry)

20,200 (0,017)

Наличие HR стратегии (s1v6_cde)

12,643 (0)

Наличие и распространение плановых ротаций (s3v8i)

11,076(0,026)

Информирование по 3-м вопросам (стратегия, финансы, организация работы) (s5v6_informed)

12,603 (0,013)

доля сотрудников-членов профсоюза (s5v1)

13,257 (0,01)

Кластер 6

Состояние рынка, на котором осуществляет деятельность компания (s6v7_recoded)

8,368 (0,015)

Относительный уровень производительности (s6v5b)

10,449 (0,034)

Относительный уровень качества услуг (s6v5a)

13,444 (0,004)

Относительный уровень рентабельности (s6v5c)

13,054 (0,011)

Доля сотрудников с высшим образованием (s6v13)

13,545 (0,009)

Доля сотрудников 50 лет и старше (s6v12)

24,750 (0)

Приложение 12

Модель мультиномиальной логистической регрессии, основанной только на независимых переменных

Кластерная модель (последний кластер - референтная группа)

B

Std. Error

Wald

df

Sig.

Exp(B)

95% Confidence Interval for Exp(B)

Lower Bound

Upper Bound

1

Intercept

-2,509

0,886

8,021

1

0,005

[s6v7_recoded=1,00]

0,028

0,96

0,001

1

0,977

1,028

0,157

6,747

[s6v7_recoded=2,00]

0,783

0,748

1,097

1

0,295

2,188

0,506

9,473

[s6v7_recoded=3,00]

0b

.

.

0

.

.

.

.

[s6v15_recoded=1,00]

2,754

1,063

6,713

1

0,01*

15,71

1,956

126,199

[s6v15_recoded=2,00]

2,267

0,893

6,444

1

0,011*

9,653

1,677

55,571

[s6v15_recoded=3,00]

2,496

1,644

2,305

1

0,129

12,138

0,484

304,663

[s6v15_recoded=4,00]

0b

.

.

0

.

.

.

.

2

Intercept

-0,904

0,584

2,392

1

0,122

[s6v7_recoded=1,00]

0,198

0,85

0,054

1

0,816

1,219

0,231

6,445

[s6v7_recoded=2,00]

0,913

0,689

1,758

1

0,185

2,491

0,646

9,606

[s6v7_recoded=3,00]

0b

.

.

0

.

.

.

.

[s6v15_recoded=1,00]

1,736

0,786

4,884

1

0,027*

5,675

1,217

26,465

[s6v15_recoded=2,00]

-1,051

0,892

1,389

1

0,239

0,35

0,061

2,007

[s6v15_recoded=3,00]

-17,98

0

.

1

.

1,55E-08

1,55E-08

1,55E-08

[s6v15_recoded=4,00]

0b

.

.

0

.

.

.

.

3

Intercept

-1,754

0,757

5,375

1

0,02

[s6v7_recoded=1,00]

-18,253

6319,782

0

1

0,998

1,18E-08

0

.c

[s6v7_recoded=2,00]

0,471

0,737

0,408

1

0,523

1,602

0,377

6,797

[s6v7_recoded=3,00]

0b

.

.

0

.

.

.

.

[s6v15_recoded=1,00]

0,907

1,34

0,459

1

0,498

2,478

0,179

34,226

[s6v15_recoded=2,00]

1,815

0,801

5,134

1

0,023*

6,143

1,278

29,535

[s6v15_recoded=3,00]

-16,889

0

.

1

.

4,62E-08

4,62E-08

4,62E-08

[s6v15_recoded=4,00]

0b

.

.

0

.

.

.

.

4 (6)

Intercept

-2,09

1,06

3,885

1

0,049

[s6v7_recoded=1,00]

-19,373

7676,918

0

1

0,998

3,86E-09

0

.c

[s6v7_recoded=2,00]

-2,013

1,185

2,886

1

0,089*

0,134

0,013

1,363

[s6v7_recoded=3,00]

0b

.

.

0

.

.

.

.

[s6v15_recoded=1,00]

2,565

1,419

3,27

1

0,071*

13,003

0,806

209,71

[s6v15_recoded=2,00]

2,421

1,212

3,992

1

0,046*

11,258

1,047

121,019

[s6v15_recoded=3,00]

2,688

1,867

2,072

1

0,15

14,698

0,378

571,142

[s6v15_recoded=4,00]

0b

.

.

0

.

.

.

.

5

Intercept

0,226

0,466

0,236

1

0,627

[s6v7_recoded=1,00]

-2,073

1,163

3,176

1

0,075*

0,126

0,013

1,23

[s6v7_recoded=2,00]

-1,751

0,874

4,014

1

0,045*

0,174

0,031

0,963

[s6v7_recoded=3,00]

0b

.

.

0

.

.

.

.

[s6v15_recoded=1,00]

0,526

0,948

0,308

1

0,579

1,693

0,264

10,861

[s6v15_recoded=2,00]

-0,887

0,912

0,946

1

0,331

0,412

0,069

2,461

[s6v15_recoded=3,00]

0,273

1,561

0,031

1

0,861

1,315

0,062

28,041

[s6v15_recoded=4,00]

0b

.

.

0

.

.

.

.

a The reference category is: 6,00.

p<0,05; Nagelkerke = 0,493

Приложение 13

Модель мультиномиальной логистической регрессии, основанной переменных, включая коррелирующие на уровне значимости p<0.1

Кластерная модель (последний кластер - референтная группа)

B

Std. Error

Wald

df

Sig.

Exp(B)

95% Confidence Interval for Exp(B)

Lower Bound

Upper Bound

1

Intercept

-2,33

1,072

4,721

1

0,03

[s6v15_recoded=1,00]

4,13

1,269

10,588

1

0,001*

62,194

5,168

748,533

[s6v15_recoded=2,00]

2,627

1,063

6,105

1

0,013*

13,829

1,721

111,097

[s6v15_recoded=3,00]

-17,539

0

.

1

.

2,42E-08

2,42E-08

2,42E-08

[s6v15_recoded=4,00]

0b

.

.

0

.

.

.

.

[s3v8c=0]

-0,816

8681,321

0

1

1

0,442

0

.c

[s3v8c=1]

-0,389

6862,461

0

1

1

0,678

0

.c

[s3v8c=2]

1,236

0,977

1,599

1

0,206

3,441

0,507

23,361

[s3v8c=3]

-1,456

1,07

1,851

1

0,174

0,233

0,029

1,9

[s3v8c=4]

0b

.

.

0

.

.

.

.

[s1v1_woman1=1,00]

0,069

0,895

0,006

1

0,938

1,072

0,185

6,196

[s1v1_woman1=2,00]

16,455

3687,914

0

1

0,996

14012793,1

0

.c

[s1v1_woman1=3,00]

-17,055

1776,203

0

1

0,992

3,92E-08

0

.c

[s1v1_woman1=4,00]

0b

.

.

0

.

.

.

.

2

Intercept

-0,827

0,744

1,233

1

0,267

[s6v15_recoded=1,00]

2,325

0,95

5,989

1

0,014*

10,223

1,589

65,776

[s6v15_recoded=2,00]

-1,074

0,994

1,168

1

0,28

0,342

0,049

2,397

[s6v15_recoded=3,00]

-18,6

0

.

1

.

8,36E-09

8,36E-09

8,36E-09

[s6v15_recoded=4,00]

0b

.

.

0

.

.

.

.

[s3v8c=0]

0,345

10011,234

0

1

1

1,411

0

.c

[s3v8c=1]

18,45

4503,572

0

1

0,997

102963672

0

.c

[s3v8c=2]

1,611

0,914

3,106

1

0,078*

5,006

0,835

30,023

[s3v8c=3]

0,408

0,865

0,223

1

0,637

1,504

0,276

8,196

[s3v8c=4]

0b

.

.

0

.

.

.

.

[s1v1_woman1=1,00]

-1,558

0,919

2,876

1

0,09*

0,211

0,035

1,275

[s1v1_woman1=2,00]

15,532

3687,914

0

1

0,997

5564735,86

0

.c

[s1v1_woman1=3,00]

0,051

0,855

0,004

1

0,952

1,052

0,197

5,622

[s1v1_woman1=4,00]

0b

.

.

0

.

.

.

.

3

Intercept

-1,324

0,86

2,373

1

0,123

[s6v15_recoded=1,00]

0,902

1,446

0,389

1

0,533

2,463

0,145

41,876

[s6v15_recoded=2,00]

1,327

0,86

2,382

1

0,123

3,769

0,699

20,321

[s6v15_recoded=3,00]

-17,941

0

.

1

.

1,62E-08

1,62E-08

1,62E-08

[s6v15_recoded=4,00]

0b

.

.

0

.

.

.

.

[s3v8c=0]

20,102

5591,053

0

1

0,997

537376834

0

.c

[s3v8c=1]

-0,065

6993,28

0

1

1

0,937

0

.c

[s3v8c=2]

1,187

1,148

1,069

1

0,301

3,276

0,346

31,053

[s3v8c=3]

1,452

0,847

2,94

1

0,086*

4,271

0,812

22,451

[s3v8c=4]

0b

.

.

0

.

.

.

.

[s1v1_woman1=1,00]

-2,528

1,13

5,01

1

0,025*

0,08

0,009

0,73

[s1v1_woman1=2,00]

-1,726

7058,485

0

1

1

0,178

0

.c

[s1v1_woman1=3,00]

-1,941

1,244

2,433

1

0,119

0,144

0,013

1,645

[s1v1_woman1=4,00]

0b

.

.

0

.

.

.

.

4 (6)

Intercept

-16,582

1668,121

0

1

0,992

[s6v15_recoded=1,00]

16,724

1668,121

0

1

0,992

18320044,5

0

.c

[s6v15_recoded=2,00]

16,395

1668,121

0

1

0,992

13187819,9

0

.c

[s6v15_recoded=3,00]

31,997

2110,763

0

1

0,988

7,8723E+13

0

.c

[s6v15_recoded=4,00]

0b

.

.

0

.

.

.

.

[s3v8c=0]

15,976

0

.

1

.

8678955,31

8678955,31

8678955,31

[s3v8c=1]

-0,413

8564,141

0

1

1

0,662

0

.c

[s3v8c=2]

-29,468

2572,876

0

1

0,991

1,59E-13

0

.c

[s3v8c=3]

0,525

1,096

0,23

1

0,632

1,691

0,197

14,483

[s3v8c=4]

0b

.

.

0

.

.

.

.

[s1v1_woman1=1,00]

-29,994

2134,91

0

1

0,989

9,41E-14

0

.c

[s1v1_woman1=2,00]

0,63

7698,271

0

1

1

1,877

0

.c

[s1v1_woman1=3,00]

-0,983

1,284

0,585

1

0,444

0,374

0,03

4,64

[s1v1_woman1=4,00]

0b

.

.

0

.

.

.

.

5

Intercept

-0,401

0,705

0,323

1

0,57

[s6v15_recoded=1,00]

0,305

1,117

0,075

1

0,785

1,357

0,152

12,123

[s6v15_recoded=2,00]

-1,645

0,994

2,737

1

0,098*

0,193

0,027

1,355

[s6v15_recoded=3,00]

1,074

1,751

0,376

1

0,54

2,926

0,094

90,603

[s6v15_recoded=4,00]

0b

.

.

0

.

.

.

.

[s3v8c=0]

19,524

5591,053

0

1

0,997

301500383

0

.c

[s3v8c=1]

18,631

4503,572

0

1

0,997

123466191

0

.c

[s3v8c=2]

0,392

1,085

0,131

1

0,718

1,48

0,177

12,413

[s3v8c=3]

1,259

0,806

2,436

1

0,119

3,52

0,725

17,096

[s3v8c=4]

0b

.

.

0

.

.

.

.

[s1v1_woman1=1,00]

-1,738

0,886

3,853

1

0,05*

0,176

0,031

0,997

[s1v1_woman1=2,00]

16,443

3687,914

0

1

0,996

13843223,1

0

.c

[s1v1_woman1=3,00]

-0,45

0,916

0,241

1

0,624

0,638

0,106

3,843

[s1v1_woman1=4,00]

0b

.

.

0

.

.

.

.

a The reference category is: 6,00.

p<0,05; Nagelkerke= 0,692

Приложение 14

Модель мультиномиальной логистической регрессии, основанной на всех переменных

Кластерная модель (последний кластер - референтная группа)

B

Std. Error

Wald

df

Sig.

Exp(B)

95% Confidence Interval for Exp(B)

1

Intercept

-0,47

0,403

1,359

1

0,244

Lower Bound

Upper Bound

[s1v1_recoded=2,00]

-0,223

1,289

0,03

1

0,863

0,8

0,064

10,014

[s1v1_recoded=3,00]

0,182

0,864

0,045

1

0,833

1,2

0,221

6,521

[s1v1_recoded=4,00]

0,152

0,615

0,061

1

0,805

1,164

0,348

3,885

[s1v1_recoded=5,00]

0b

.

.

0

.

.

.

.

2

Intercept

-1,386

0,559

6,15

1

0,013

[s1v1_recoded=2,00]

3,178

0,946

11,274

1

0,001*

24

3,755

153,412

[s1v1_recoded=3,00]

1,386

0,901

2,365

1

0,124

4

0,684

23,406

[s1v1_recoded=4,00]

0,087

0,858

0,01

1

0,919

1,091

0,203

5,867

[s1v1_recoded=5,00]

0b

.

.

0

.

.

.

.

3

Intercept

-0,47

0,403

1,359

1

0,244

[s1v1_recoded=2,00]

0,47

1,078

0,19

1

0,663

1,6

0,193

13,24

[s1v1_recoded=3,00]

-19,525

0

.

1

.

3,32E-09

3,32E-09

3,32E-09

[s1v1_recoded=4,00]

-0,829

0,766

1,172

1

0,279

0,436

0,097

1,958

[s1v1_recoded=5,00]

0b

.

.

0

.

.

.

.

4(6)

Intercept

-0,827

0,453

3,328

1

0,068

[s1v1_recoded=2,00]

-18,837

0

.

1

.

6,60E-09

6,60E-09

6,60E-09

[s1v1_recoded=3,00]

-19,574

0

.

1

.

3,16E-09

3,16E-09

3,16E-09

[s1v1_recoded=4,00]

-0,473

0,793

0,355

1

0,551

0,623

0,132

2,952

[s1v1_recoded=5,00]

0b

.

.

0

.

.

.

.

5

Intercept

-0,827

0,453

3,328

1

0,068

[s1v1_recoded=2,00]

1,925

0,934

4,251

1

0,039*

6,857

1,1

42,758

[s1v1_recoded=3,00]

0,539

0,888

0,368

1

0,544

1,714

0,301

9,773

[s1v1_recoded=4,00]

-0,878

0,892

0,968

1

0,325

0,416

0,072

2,389

[s1v1_recoded=5,00]

0b

.

.

0

.

.

.

.

p<0,05; Nagelkerke = 0,304

Приложение 15

Логлинейный анализ для учета влияния отрасли

Parameter

Estimate

Std. Error

Z

Sig.

95% Confidence Interval

Lower Bound

Upper Bound

Constant

2,015

0,365

5,518

0

1,299

2,731

[F1_Cl_6Cosine_WG_recoded = 1,00]

-0,31

0,561

-0,552

0,581

-1,41

0,79

[F1_Cl_6Cosine_WG_recoded = 2,00]

-1,38E-16

0,516

0

1

-1,012

1,012

[F1_Cl_6Cosine_WG_recoded = 3,00]

0,236

0,488

0,484

0,628

-0,721

1,194

[F1_Cl_6Cosine_WG_recoded = 4,00]

-0,511

0,596

-0,857

0,392

-1,68

0,658

[F1_Cl_6Cosine_WG_recoded = 5,00]

-1,609

0,894

-1,799

0,072

-3,362

0,144

[F1_Cl_6Cosine_WG_recoded = 6,00]

0a

.

.

.

.

.

[s6v1_industry =,00]

0,125

0,501

0,25

0,803

-0,857

1,107

[s6v1_industry = 3,00]

-0,762

0,647

-1,177

0,239

-2,031

0,507

[s6v1_industry = 4,00]

-0,143

0,536

-0,267

0,789

-1,193

0,907

[s6v1_industry = 5,00]

-1,609

0,894

-1,799

0,072*

-3,362

0,144

[s6v1_industry = 6,00]

-1,609

0,894

-1,799

0,072*

-3,362

0,144

[s6v1_industry = 7,00]

-0,511

0,596

-0,857

0,392

-1,68

0,658

[s6v1_industry = 8,00]

-1,609

0,894

-1,799

0,072*

-3,362

0,144

[s6v1_industry = 10,00]

-2,708

1,461

-1,854

0,064*

-5,571

0,155

[s6v1_industry = 11,00]

-1,609

0,894

-1,799

0,072*

-3,362

0,144

[s6v1_industry = 12,00]

0a

.

.

.

.

.

[F1_Cl_6Cosine_WG_recoded = 1,00] * [s6v1_industry =,00]

-0,125

0,784

-0,16

0,873

-1,662

1,411

[F1_Cl_6Cosine_WG_recoded = 1,00] * [s6v1_industry = 3,00]

0,561

0,907

0,619

0,536

-1,217

2,34

[F1_Cl_6Cosine_WG_recoded = 1,00] * [s6v1_industry = 4,00]

-0,058

0,831

-0,069

0,945

-1,687

1,572

[F1_Cl_6Cosine_WG_recoded = 1,00] * [s6v1_industry = 5,00]

-0,788

1,727

-0,457

0,648

-4,173

2,596

[F1_Cl_6Cosine_WG_recoded = 1,00] * [s6v1_industry = 6,00]

-0,788

1,727

-0,457

0,648

-4,173

2,596

[F1_Cl_6Cosine_WG_recoded = 1,00] * [s6v1_industry = 7,00]

-1,887

1,593

-1,185

0,236

-5,009

1,235

[F1_Cl_6Cosine_WG_recoded = 1,00] * [s6v1_industry = 8,00]

0,821

1,176

0,698

0,485

-1,483

3,125

[F1_Cl_6Cosine_WG_recoded = 1,00] * [s6v1_industry = 10,00]

1,409

1,727

0,816

0,415

-1,976

4,793

[F1_Cl_6Cosine_WG_recoded = 1,00] * [s6v1_industry = 11,00]

0,31

1,284

0,242

0,809

-2,206

2,827

[F1_Cl_6Cosine_WG_recoded = 1,00] * [s6v1_industry = 12,00]

0a

.

.

.

.

.

[F1_Cl_6Cosine_WG_recoded = 2,00] * [s6v1_industry =,00]

-0,887

0,819

-1,084

0,278

-2,492

0,717

[F1_Cl_6Cosine_WG_recoded = 2,00] * [s6v1_industry = 3,00]

-1,946

1,598

-1,218

0,223

-5,077

1,185

[F1_Cl_6Cosine_WG_recoded = 2,00] * [s6v1_industry = 4,00]

-2,565

1,556

-1,649

0,099 *

-5,614

0,484

[F1_Cl_6Cosine_WG_recoded = 2,00] * [s6v1_industry = 5,00]

-3,70E-17

1,265

0

1

-2,479

2,479

[F1_Cl_6Cosine_WG_recoded = 2,00] * [s6v1_industry = 6,00]

1,88E-16

1,265

0

1

-2,479

2,479

[F1_Cl_6Cosine_WG_recoded = 2,00] * [s6v1_industry = 7,00]

0,636

0,779

0,817

0,414

-0,89

2,162

[F1_Cl_6Cosine_WG_recoded = 2,00] * [s6v1_industry = 8,00]

0,847

1,104

0,767

0,443

-1,317

3,011

[F1_Cl_6Cosine_WG_recoded = 2,00] * [s6v1_industry = 10,00]

1,84E-16

2,066

0

1

-4,048

4,048

[F1_Cl_6Cosine_WG_recoded = 2,00] * [s6v1_industry = 11,00]

-1,099

1,713

-0,641

0,521

-4,455

2,258

[F1_Cl_6Cosine_WG_recoded = 2,00] * [s6v1_industry = 12,00]

0a

.

.

.

.

.

[F1_Cl_6Cosine_WG_recoded = 3,00] * [s6v1_industry =,00]

-1,124

0,801

-1,402

0,161

-2,694

0,447

[F1_Cl_6Cosine_WG_recoded = 3,00] * [s6v1_industry = 3,00]

-2,182

1,589

-1,374

0,17

-5,296

0,932

[F1_Cl_6Cosine_WG_recoded = 3,00] * [s6v1_industry = 4,00]

-2,801

1,547

-1,811

0,07*

-5,833

0,23

[F1_Cl_6Cosine_WG_recoded = 3,00] * [s6v1_industry = 5,00]

0,274

1,142

0,24

0,81

-1,965

2,514

[F1_Cl_6Cosine_WG_recoded = 3,00] * [s6v1_industry = 6,00]

-1,335

1,704

-0,783

0,433

-4,676

2,006

[F1_Cl_6Cosine_WG_recoded = 3,00] * [s6v1_industry = 7,00]

-2,434

1,569

-1,551

0,121

-5,508

0,641

[F1_Cl_6Cosine_WG_recoded = 3,00] * [s6v1_industry = 8,00]

-1,335

1,704

-0,783

0,433

-4,676

2,006

[F1_Cl_6Cosine_WG_recoded = 3,00] * [s6v1_industry = 10,00]

-0,236

2,059

-0,115

0,909

-4,272

3,799

[F1_Cl_6Cosine_WG_recoded = 3,00] * [s6v1_industry = 11,00]

-0,236

1,254

-0,189

0,85

-2,694

2,221

[F1_Cl_6Cosine_WG_recoded = 3,00] * [s6v1_industry = 12,00]

0a

.

.

.

.

.

[F1_Cl_6Cosine_WG_recoded = 4,00] * [s6v1_industry =,00]

-0,376

0,871

-0,432

0,666

-2,084

1,331

[F1_Cl_6Cosine_WG_recoded = 4,00] * [s6v1_industry = 3,00]

-1,435

1,625

-0,883

0,377

-4,62

1,75

[F1_Cl_6Cosine_WG_recoded = 4,00] * [s6v1_industry = 4,00]

-0,445

0,954

-0,466

0,641

-2,314

1,424

[F1_Cl_6Cosine_WG_recoded = 4,00] * [s6v1_industry = 5,00]

-0,588

1,738

-0,338

0,735

-3,995

2,82

[F1_Cl_6Cosine_WG_recoded = 4,00] * [s6v1_industry = 6,00]

-0,588

1,738

-0,338

0,735

-3,995

2,82

[F1_Cl_6Cosine_WG_recoded = 4,00] * [s6v1_industry = 7,00]

-1,686

1,606

-1,05

0,294

-4,833

1,46

[F1_Cl_6Cosine_WG_recoded = 4,00] * [s6v1_industry = 8,00]

0,511

1,3

0,393

0,694

-2,036

3,058

[F1_Cl_6Cosine_WG_recoded = 4,00] * [s6v1_industry = 10,00]

0,511

2,087

0,245

0,807

-3,58

4,601

[F1_Cl_6Cosine_WG_recoded = 4,00] * [s6v1_industry = 11,00]

-0,588

1,738

-0,338

0,735

-3,995

2,82

[F1_Cl_6Cosine_WG_recoded = 4,00] * [s6v1_industry = 12,00]

0a

.

.

.

.

.

[F1_Cl_6Cosine_WG_recoded = 5,00] * [s6v1_industry =,00]

-1,224

1,708

-0,716

0,474

-4,572

2,124

[F1_Cl_6Cosine_WG_recoded = 5,00] * [s6v1_industry = 3,00]

1,273

1,219

1,044

0,296

-1,116

3,662

[F1_Cl_6Cosine_WG_recoded = 5,00] * [s6v1_industry = 4,00]

1,442

1,066

1,353

0,176

-0,646

3,531

[F1_Cl_6Cosine_WG_recoded = 5,00] * [s6v1_industry = 5,00]

2,457

1,324

1,856

0,063*

-0,138

5,051

[F1_Cl_6Cosine_WG_recoded = 5,00] * [s6v1_industry = 6,00]

2,12

1,366

1,552

0,121

-0,558

4,798

[F1_Cl_6Cosine_WG_recoded = 5,00] * [s6v1_industry = 7,00]

1,358

1,144

1,188

0,235

-0,883

3,6

[F1_Cl_6Cosine_WG_recoded = 5,00] * [s6v1_industry = 8,00]

2,12

1,366

1,552

0,121

-0,558

4,798

[F1_Cl_6Cosine_WG_recoded = 5,00] * [s6v1_industry = 10,00]

1,609

2,191

0,735

0,463

-2,685

5,904

[F1_Cl_6Cosine_WG_recoded = 5,00] * [s6v1_industry = 11,00]

0,511

1,862

0,274

0,784

-3,138

4,16

[F1_Cl_6Cosine_WG_recoded = 5,00] * [s6v1_industry = 12,00]

0a

.

.

.

.

.

[F1_Cl_6Cosine_WG_recoded = 6,00] * [s6v1_industry =,00]

0a

.

.

.

.

.

[F1_Cl_6Cosine_WG_recoded = 6,00] * [s6v1_industry = 3,00]

0a

.

.

.

.

.

[F1_Cl_6Cosine_WG_recoded = 6,00] * [s6v1_industry = 4,00]

0a

.

.

.

.

.

[F1_Cl_6Cosine_WG_recoded = 6,00] * [s6v1_industry = 5,00]

0a

.

.

.

.

.

[F1_Cl_6Cosine_WG_recoded = 6,00] * [s6v1_industry = 6,00]

0a

.

.

.

.

.

[F1_Cl_6Cosine_WG_recoded = 6,00] * [s6v1_industry = 7,00]

0a

.

.

.

.

.

[F1_Cl_6Cosine_WG_recoded = 6,00] * [s6v1_industry = 8,00]

0a

.

.

.

.

.

[F1_Cl_6Cosine_WG_recoded = 6,00] * [s6v1_industry = 10,00]

0a

.

.

.

.

.

[F1_Cl_6Cosine_WG_recoded = 6,00] * [s6v1_industry = 11,00]

0a

.

.

.

.

.

[F1_Cl_6Cosine_WG_recoded = 6,00] * [s6v1_industry = 12,00]

0a

.

.

.

.

.

a This parameter is set to zero because it is redundant.

b Model: Poisson

c Design: Constant + F1_Cl_6Cosine_WG_recoded + s6v1_industry + F1_Cl_6Cosine_WG_recoded * s6v1_industry