Approach 2: gendered models
FOR MEN
- Voluntariness
##
## Call:
## lm(formula = voluntary ~ careertype + age + education + sector,
## data = mydata, subset = gender == "male")
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.7478 -0.1849 0.0306 0.2789 0.4957
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.03862 0.07300 -0.53 0.5969
## careertypeHT 0.01858 0.04178 0.44 0.6567
## careertypeIM -0.12002 0.04369 -2.75 0.0061 **
## age 0.00102 0.00104 0.98 0.3280
## education 0.00454 0.00274 1.66 0.0973 .
## sector1 -0.08796 0.05286 -1.66 0.0965 .
## sector2 -0.06378 0.07052 -0.90 0.3660
## sector3 -0.04785 0.02534 -1.89 0.0593 .
## sector4 0.04000 0.05432 0.74 0.4617
## sector5 -0.04381 0.03301 -1.33 0.1848
## sector6 -0.05370 0.03245 -1.66 0.0983 .
## sector7 -0.00226 0.07482 -0.03 0.9760
## sector8 0.01124 0.03584 0.31 0.7540
## sector9 0.08290 0.05348 1.55 0.1215
## sector10 0.22322 0.15862 1.41 0.1597
## sector11 0.01847 0.02963 0.62 0.5331
## sector12 0.01272 0.03680 0.35 0.7296
## sector13 -0.05162 0.04773 -1.08 0.2798
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.294 on 881 degrees of freedom
## (324 observations deleted due to missingness)
## Multiple R-squared: 0.0378, Adjusted R-squared: 0.0192
## F-statistic: 2.04 on 17 and 881 DF, p-value: 0.00782
- Age - included as control = SKIP
SUBJECTIVE CAREER SUCCESS INDICATORS
- Career satisfaction
##
## Call:
## lm(formula = satisfaction ~ careertype + age + education + sector,
## data = mydata, subset = gender == "male")
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.355 -0.397 -0.169 0.568 1.349
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.60115 0.20112 -2.99 0.00289 **
## careertypeHT 0.18230 0.10154 1.80 0.07300 .
## careertypeIM -0.45560 0.09851 -4.63 4.4e-06 ***
## age 0.00958 0.00269 3.56 0.00039 ***
## education 0.01403 0.00651 2.16 0.03146 *
## sector1 0.22950 0.11192 2.05 0.04064 *
## sector2 -0.20321 0.17423 -1.17 0.24383
## sector3 -0.04081 0.06815 -0.60 0.54951
## sector4 0.32429 0.13046 2.49 0.01314 *
## sector5 0.06129 0.08446 0.73 0.46830
## sector6 0.10394 0.08468 1.23 0.22003
## sector7 -0.18618 0.19480 -0.96 0.33949
## sector8 -0.03770 0.09835 -0.38 0.70154
## sector9 0.12249 0.13239 0.93 0.35514
## sector10 -0.60475 0.60131 -1.01 0.31486
## sector11 0.10887 0.08057 1.35 0.17703
## sector12 0.03194 0.08862 0.36 0.71861
## sector13 -0.02928 0.12001 -0.24 0.80729
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.638 on 780 degrees of freedom
## (425 observations deleted due to missingness)
## Multiple R-squared: 0.0907, Adjusted R-squared: 0.0708
## F-statistic: 4.57 on 17 and 780 DF, p-value: 3.12e-09
- Career disappointment
##
## Call:
## lm(formula = disappointment ~ careertype + age + education +
## sector, data = mydata, subset = gender == "male")
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8119 -0.8084 -0.0161 0.4471 2.4736
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.09211 0.28256 3.87 0.00012 ***
## careertypeHT 0.14427 0.14270 1.01 0.31232
## careertypeIM 0.52376 0.13843 3.78 0.00017 ***
## age -0.01464 0.00378 -3.88 0.00012 ***
## education 0.01384 0.00915 1.51 0.13087
## sector1 -0.17495 0.15727 -1.11 0.26630
## sector2 0.10407 0.24484 0.43 0.67092
## sector3 -0.17236 0.09577 -1.80 0.07230 .
## sector4 -0.25352 0.18334 -1.38 0.16713
## sector5 -0.07548 0.11869 -0.64 0.52501
## sector6 0.13470 0.11900 1.13 0.25804
## sector7 0.21942 0.27376 0.80 0.42309
## sector8 -0.23186 0.13821 -1.68 0.09381 .
## sector9 0.07722 0.18605 0.42 0.67820
## sector10 0.72490 0.84503 0.86 0.39124
## sector11 -0.04716 0.11294 -0.42 0.67640
## sector12 -0.27458 0.12454 -2.20 0.02776 *
## sector13 -0.02954 0.16866 -0.18 0.86099
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.897 on 781 degrees of freedom
## (424 observations deleted due to missingness)
## Multiple R-squared: 0.0674, Adjusted R-squared: 0.0471
## F-statistic: 3.32 on 17 and 781 DF, p-value: 6.72e-06
- Career achievements
##
## Call:
## lm(formula = achievements ~ careertype + age + education + sector,
## data = mydata, subset = gender == "male")
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.313 -0.317 -0.207 0.625 1.263
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.38867 0.19574 -1.99 0.0474 *
## careertypeHT 0.10906 0.09886 1.10 0.2703
## careertypeIM -0.31957 0.09589 -3.33 0.0009 ***
## age 0.00599 0.00262 2.29 0.0223 *
## education 0.00880 0.00634 1.39 0.1652
## sector1 0.24414 0.10895 2.24 0.0253 *
## sector2 -0.38278 0.16961 -2.26 0.0243 *
## sector3 -0.01568 0.06635 -0.24 0.8132
## sector4 0.18894 0.12700 1.49 0.1372
## sector5 0.03962 0.08223 0.48 0.6300
## sector6 0.01492 0.08244 0.18 0.8565
## sector7 0.07167 0.18964 0.38 0.7056
## sector8 0.06081 0.09574 0.64 0.5255
## sector9 -0.02619 0.12888 -0.20 0.8390
## sector10 -0.42724 0.58537 -0.73 0.4657
## sector11 -0.03140 0.07824 -0.40 0.6883
## sector12 0.15282 0.08663 1.76 0.0781 .
## sector13 0.06026 0.11683 0.52 0.6061
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.621 on 780 degrees of freedom
## (425 observations deleted due to missingness)
## Multiple R-squared: 0.0553, Adjusted R-squared: 0.0347
## F-statistic: 2.68 on 17 and 780 DF, p-value: 0.000264
- Career sacrifices
##
## Call:
## lm(formula = sacrifices ~ careertype + age + education + sector,
## data = mydata, subset = gender == "male")
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.733 -0.378 -0.173 0.694 1.950
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.70438 0.25905 2.72 0.0067 **
## careertypeHT -0.02767 0.13067 -0.21 0.8323
## careertypeIM -0.19673 0.12675 -1.55 0.1211
## age -0.00762 0.00346 -2.20 0.0281 *
## education 0.01464 0.00841 1.74 0.0823 .
## sector1 -0.17696 0.14401 -1.23 0.2195
## sector2 -0.21538 0.22419 -0.96 0.3370
## sector3 -0.09309 0.08770 -1.06 0.2888
## sector4 -0.04808 0.16788 -0.29 0.7746
## sector5 -0.09217 0.10868 -0.85 0.3966
## sector6 0.25902 0.10897 2.38 0.0177 *
## sector7 0.29176 0.25067 1.16 0.2448
## sector8 -0.14755 0.12655 -1.17 0.2440
## sector9 0.12362 0.17036 0.73 0.4683
## sector10 0.63025 0.77377 0.81 0.4156
## sector11 -0.12492 0.10343 -1.21 0.2275
## sector12 -0.11357 0.11408 -1.00 0.3198
## sector13 0.00514 0.15445 0.03 0.9735
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.821 on 780 degrees of freedom
## (425 observations deleted due to missingness)
## Multiple R-squared: 0.0418, Adjusted R-squared: 0.0209
## F-statistic: 2 on 17 and 780 DF, p-value: 0.00932
- Health suffered due to career
##
## Call:
## lm(formula = healthsuffered ~ careertype + age + education +
## sector, data = mydata, subset = gender == "male")
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.7669 -0.8804 -0.0896 0.7470 2.1561
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.83555 0.28407 2.94 0.0034 **
## careertypeHT 0.16021 0.14335 1.12 0.2641
## careertypeIM 0.34033 0.13905 2.45 0.0146 *
## age -0.01164 0.00380 -3.06 0.0023 **
## education -0.01286 0.00919 -1.40 0.1620
## sector1 0.04129 0.15798 0.26 0.7939
## sector2 0.34924 0.24593 1.42 0.1560
## sector3 0.01915 0.09628 0.20 0.8424
## sector4 -0.09397 0.18415 -0.51 0.6100
## sector5 -0.01594 0.11922 -0.13 0.8937
## sector6 0.19514 0.11954 1.63 0.1030
## sector7 0.03141 0.27498 0.11 0.9091
## sector8 -0.11765 0.13882 -0.85 0.3970
## sector9 -0.12394 0.18688 -0.66 0.5074
## sector10 -0.13502 0.84879 -0.16 0.8737
## sector11 0.05560 0.11344 0.49 0.6242
## sector12 -0.00729 0.12509 -0.06 0.9535
## sector13 -0.04910 0.16941 -0.29 0.7720
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.901 on 780 degrees of freedom
## (425 observations deleted due to missingness)
## Multiple R-squared: 0.0374, Adjusted R-squared: 0.0164
## F-statistic: 1.78 on 17 and 780 DF, p-value: 0.0261
MARITAL INDICATORS
- Number of marriages
##
## Call:
## lm(formula = marriages ~ careertype + age + education + sector,
## data = mydata, subset = gender == "male")
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.1511 -0.0975 -0.0542 -0.0147 2.8691
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.07e-02 9.35e-02 0.97 0.3326
## careertypeHT 1.54e-01 5.95e-02 2.59 0.0097 **
## careertypeIM 2.55e-02 6.21e-02 0.41 0.6807
## age -1.61e-03 1.34e-03 -1.21 0.2278
## education 6.75e-06 3.58e-03 0.00 0.9985
## sector1 -9.94e-02 5.96e-02 -1.67 0.0955 .
## sector2 1.14e-01 9.28e-02 1.23 0.2189
## sector3 7.90e-03 3.28e-02 0.24 0.8095
## sector4 -2.32e-02 7.35e-02 -0.32 0.7524
## sector5 1.24e-02 4.25e-02 0.29 0.7702
## sector6 3.80e-02 4.21e-02 0.90 0.3679
## sector7 7.08e-02 1.07e-01 0.66 0.5075
## sector8 -1.65e-02 4.88e-02 -0.34 0.7349
## sector9 -3.28e-02 6.80e-02 -0.48 0.6295
## sector10 -9.13e-02 1.97e-01 -0.46 0.6429
## sector11 6.72e-02 3.89e-02 1.72 0.0849 .
## sector12 -3.36e-02 4.57e-02 -0.73 0.4630
## sector13 5.98e-02 5.90e-02 1.01 0.3109
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.421 on 1081 degrees of freedom
## (124 observations deleted due to missingness)
## Multiple R-squared: 0.0199, Adjusted R-squared: 0.00446
## F-statistic: 1.29 on 17 and 1081 DF, p-value: 0.191
- Number of children
##
## Call:
## lm(formula = children ~ careertype + age + education + sector,
## data = mydata, subset = gender == "male")
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.343 -0.735 -0.223 0.621 7.220
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.484895 0.297804 -4.99 7.3e-07 ***
## careertypeHT -0.106084 0.183418 -0.58 0.56315
## careertypeIM -0.473377 0.204640 -2.31 0.02092 *
## age 0.023293 0.004245 5.49 5.2e-08 ***
## education 0.000319 0.011212 0.03 0.97729
## sector1 0.353196 0.190289 1.86 0.06375 .
## sector2 -0.081049 0.292595 -0.28 0.78184
## sector3 -0.063069 0.102198 -0.62 0.53730
## sector4 -0.814567 0.237549 -3.43 0.00063 ***
## sector5 -0.023010 0.134205 -0.17 0.86390
## sector6 -0.256601 0.131230 -1.96 0.05084 .
## sector7 0.083181 0.311068 0.27 0.78922
## sector8 0.030686 0.153853 0.20 0.84195
## sector9 -0.291283 0.213008 -1.37 0.17180
## sector10 1.252985 0.660254 1.90 0.05804 .
## sector11 -0.194647 0.121316 -1.60 0.10895
## sector12 0.065470 0.142862 0.46 0.64686
## sector13 0.065069 0.185930 0.35 0.72644
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.22 on 946 degrees of freedom
## (259 observations deleted due to missingness)
## Multiple R-squared: 0.0636, Adjusted R-squared: 0.0468
## F-statistic: 3.78 on 17 and 946 DF, p-value: 3.74e-07
- Number of divorces
##
## Call:
## lm(formula = divorces ~ careertype + age + education + sector,
## data = mydata, subset = gender == "male")
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4929 -0.2186 -0.1417 -0.0445 2.6648
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.46666 0.09466 4.93 9.5e-07 ***
## careertypeHT 0.13600 0.06019 2.26 0.0240 *
## careertypeIM 0.17636 0.06279 2.81 0.0051 **
## age -0.00741 0.00135 -5.48 5.4e-08 ***
## education -0.00306 0.00363 -0.84 0.3994
## sector1 -0.09369 0.06028 -1.55 0.1204
## sector2 0.06001 0.09391 0.64 0.5229
## sector3 -0.01129 0.03317 -0.34 0.7336
## sector4 -0.03158 0.07440 -0.42 0.6713
## sector5 0.00231 0.04303 0.05 0.9573
## sector6 0.04672 0.04265 1.10 0.2736
## sector7 0.15984 0.10800 1.48 0.1392
## sector8 -0.08566 0.04941 -1.73 0.0833 .
## sector9 -0.02252 0.06884 -0.33 0.7436
## sector10 -0.21010 0.19923 -1.05 0.2919
## sector11 0.05751 0.03940 1.46 0.1447
## sector12 0.02184 0.04624 0.47 0.6369
## sector13 0.04820 0.05972 0.81 0.4198
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.426 on 1081 degrees of freedom
## (124 observations deleted due to missingness)
## Multiple R-squared: 0.0541, Adjusted R-squared: 0.0392
## F-statistic: 3.63 on 17 and 1081 DF, p-value: 8.68e-07
FINANCIALS
- Wage Main Job
##
## Call:
## lm(formula = WageMain ~ careertype + age + education + sector +
## divorces + children + activecareer + voluntary + satisfaction +
## disappointment, data = mydata, subset = gender == "male")
##
## Residuals:
## Min 1Q Median 3Q Max
## -2134 -510 -128 347 11054
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2778.73 481.86 5.77 1.8e-08 ***
## careertypeHT 24.34 196.96 0.12 0.9017
## careertypeIM 315.94 260.94 1.21 0.2268
## age -18.59 6.93 -2.68 0.0077 **
## education 134.41 14.70 9.14 < 2e-16 ***
## sector1 -783.71 303.22 -2.58 0.0101 *
## sector2 335.34 366.65 0.91 0.3610
## sector3 239.18 136.99 1.75 0.0817 .
## sector4 292.69 298.22 0.98 0.3270
## sector5 63.08 182.14 0.35 0.7293
## sector6 -68.84 177.60 -0.39 0.6985
## sector7 189.91 369.00 0.51 0.6071
## sector8 117.60 206.62 0.57 0.5696
## sector9 553.38 278.55 1.99 0.0477 *
## sector10 -870.83 951.19 -0.92 0.3605
## sector11 189.88 161.13 1.18 0.2394
## sector12 -154.72 190.76 -0.81 0.4179
## sector13 183.71 247.45 0.74 0.4583
## divorces -41.11 131.37 -0.31 0.7545
## children 102.91 45.17 2.28 0.0233 *
## activecareer 28.40 9.78 2.90 0.0039 **
## voluntary 10.05 186.07 0.05 0.9570
## satisfaction 137.56 86.22 1.60 0.1115
## disappointment -131.45 61.24 -2.15 0.0325 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 998 on 357 degrees of freedom
## (842 observations deleted due to missingness)
## Multiple R-squared: 0.274, Adjusted R-squared: 0.227
## F-statistic: 5.86 on 23 and 357 DF, p-value: 1.13e-14
- Last Salary
##
## Call:
## lm(formula = lastsalary ~ careertype + age + education + sector +
## divorces + children + activecareer + voluntary + satisfaction +
## disappointment, data = mydata, subset = gender == "male")
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.010 -1.953 0.264 2.258 4.624
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.534981 1.413819 2.50 0.013 *
## careertypeHT 1.140343 0.530928 2.15 0.032 *
## careertypeIM 0.315633 1.238087 0.25 0.799
## age -0.033080 0.019747 -1.68 0.095 .
## education 0.054364 0.042238 1.29 0.199
## sector1 -1.220342 1.107881 -1.10 0.271
## sector2 1.425429 0.954688 1.49 0.136
## sector3 -0.360151 0.381529 -0.94 0.346
## sector4 -0.557859 0.659568 -0.85 0.398
## sector5 -0.677256 0.513053 -1.32 0.188
## sector6 -0.609823 0.511725 -1.19 0.234
## sector7 1.096620 1.094452 1.00 0.317
## sector8 -0.946838 0.540102 -1.75 0.080 .
## sector9 0.840879 0.761276 1.10 0.270
## sector10 0.050332 2.619025 0.02 0.985
## sector11 -0.286651 0.423763 -0.68 0.499
## sector12 0.459175 0.491665 0.93 0.351
## sector13 0.488799 0.740248 0.66 0.509
## divorces 0.063076 0.394034 0.16 0.873
## children 0.179535 0.121589 1.48 0.141
## activecareer -0.032026 0.032018 -1.00 0.318
## voluntary -0.553154 0.504489 -1.10 0.274
## satisfaction 0.456745 0.252413 1.81 0.071 .
## disappointment 0.000536 0.173326 0.00 0.998
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.75 on 370 degrees of freedom
## (829 observations deleted due to missingness)
## Multiple R-squared: 0.0964, Adjusted R-squared: 0.0402
## F-statistic: 1.72 on 23 and 370 DF, p-value: 0.0223
- Home ownership
## Error: contrasts can be applied only to factors with 2 or more levels
##
## Call:
## lm(formula = lastsalary ~ careertype + age + education + sector +
## divorces + children + activecareer + voluntary + satisfaction +
## disappointment, data = mydata, subset = gender == "male")
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.010 -1.953 0.264 2.258 4.624
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.534981 1.413819 2.50 0.013 *
## careertypeHT 1.140343 0.530928 2.15 0.032 *
## careertypeIM 0.315633 1.238087 0.25 0.799
## age -0.033080 0.019747 -1.68 0.095 .
## education 0.054364 0.042238 1.29 0.199
## sector1 -1.220342 1.107881 -1.10 0.271
## sector2 1.425429 0.954688 1.49 0.136
## sector3 -0.360151 0.381529 -0.94 0.346
## sector4 -0.557859 0.659568 -0.85 0.398
## sector5 -0.677256 0.513053 -1.32 0.188
## sector6 -0.609823 0.511725 -1.19 0.234
## sector7 1.096620 1.094452 1.00 0.317
## sector8 -0.946838 0.540102 -1.75 0.080 .
## sector9 0.840879 0.761276 1.10 0.270
## sector10 0.050332 2.619025 0.02 0.985
## sector11 -0.286651 0.423763 -0.68 0.499
## sector12 0.459175 0.491665 0.93 0.351
## sector13 0.488799 0.740248 0.66 0.509
## divorces 0.063076 0.394034 0.16 0.873
## children 0.179535 0.121589 1.48 0.141
## activecareer -0.032026 0.032018 -1.00 0.318
## voluntary -0.553154 0.504489 -1.10 0.274
## satisfaction 0.456745 0.252413 1.81 0.071 .
## disappointment 0.000536 0.173326 0.00 0.998
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.75 on 370 degrees of freedom
## (829 observations deleted due to missingness)
## Multiple R-squared: 0.0964, Adjusted R-squared: 0.0402
## F-statistic: 1.72 on 23 and 370 DF, p-value: 0.0223
CAREER ACTIVITY
- Retirement age
##
## Call:
## lm(formula = AgePension ~ careertype + age + education + sector +
## divorces + children + activecareer + voluntary + satisfaction +
## disappointment, data = mydata, subset = gender == "male")
##
## Residuals:
## Min 1Q Median 3Q Max
## -15.008 -2.159 -0.396 1.669 13.342
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 48.0792 1.5854 30.33 < 2e-16 ***
## careertypeHT -0.4669 0.6315 -0.74 0.46005
## careertypeIM 10.2736 1.4115 7.28 1.4e-12 ***
## age 0.0975 0.0217 4.49 9.1e-06 ***
## education 0.3526 0.0490 7.19 2.6e-12 ***
## sector1 1.3412 0.9708 1.38 0.16778
## sector2 -1.2264 1.1869 -1.03 0.30198
## sector3 -0.1420 0.4510 -0.31 0.75298
## sector4 -1.5355 0.8571 -1.79 0.07387 .
## sector5 0.3793 0.5854 0.65 0.51729
## sector6 0.5383 0.6128 0.88 0.38014
## sector7 -0.8111 1.2496 -0.65 0.51663
## sector8 -0.7123 0.6620 -1.08 0.28245
## sector9 1.9526 0.9650 2.02 0.04358 *
## sector10 -1.0875 3.4479 -0.32 0.75260
## sector11 0.0485 0.5244 0.09 0.92639
## sector12 0.2730 0.6137 0.44 0.65663
## sector13 0.9855 0.8689 1.13 0.25726
## divorces 0.8373 0.4784 1.75 0.08072 .
## children 0.0683 0.1334 0.51 0.60904
## activecareer 0.5860 0.0320 18.31 < 2e-16 ***
## voluntary -1.1531 0.5756 -2.00 0.04569 *
## satisfaction 0.9018 0.2966 3.04 0.00249 **
## disappointment 0.7800 0.2036 3.83 0.00015 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.63 on 472 degrees of freedom
## (727 observations deleted due to missingness)
## Multiple R-squared: 0.525, Adjusted R-squared: 0.502
## F-statistic: 22.7 on 23 and 472 DF, p-value: <2e-16
- Career active years
## Warning: the response appeared on the right-hand side and was dropped
## Warning: problem with term 7 in model.matrix: no columns are assigned
##
## Call:
## lm(formula = activecareer ~ careertype + age + education + sector +
## divorces + children + activecareer + voluntary + satisfaction +
## disappointment, data = mydata, subset = gender == "male")
##
## Residuals:
## Min 1Q Median 3Q Max
## -39.80 -2.93 0.57 3.25 15.88
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.6153 2.0306 -2.27 0.02340 *
## careertypeHT 1.7416 0.9212 1.89 0.05920 .
## careertypeIM -13.2833 0.9990 -13.30 < 2e-16 ***
## age 0.1643 0.0277 5.93 5.2e-09 ***
## education -0.3375 0.0648 -5.21 2.6e-07 ***
## sector1 4.2219 1.2628 3.34 0.00088 ***
## sector2 -0.9950 1.5918 -0.63 0.53216
## sector3 -1.0374 0.6229 -1.67 0.09637 .
## sector4 0.2254 1.2321 0.18 0.85490
## sector5 -0.0192 0.7914 -0.02 0.98064
## sector6 -0.6844 0.8049 -0.85 0.39550
## sector7 -1.1859 1.6518 -0.72 0.47309
## sector8 0.6195 0.9182 0.67 0.50017
## sector9 -1.1869 1.2775 -0.93 0.35325
## sector10 -0.8069 5.1002 -0.16 0.87434
## sector11 0.7368 0.7486 0.98 0.32540
## sector12 -1.2671 0.8804 -1.44 0.15063
## sector13 -0.9598 1.1136 -0.86 0.38910
## divorces -0.2425 0.5772 -0.42 0.67455
## children -0.1608 0.1793 -0.90 0.37013
## voluntary 2.8191 0.7789 3.62 0.00032 ***
## satisfaction 1.1646 0.3690 3.16 0.00168 **
## disappointment 0.1574 0.2604 0.60 0.54578
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.38 on 573 degrees of freedom
## (627 observations deleted due to missingness)
## Multiple R-squared: 0.426, Adjusted R-squared: 0.404
## F-statistic: 19.3 on 22 and 573 DF, p-value: <2e-16
- Career length
##
## Call:
## lm(formula = lastsalary ~ careertype + age + education + sector +
## divorces + children + activecareer + voluntary + satisfaction +
## disappointment, data = mydata, subset = gender == "male")
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.010 -1.953 0.264 2.258 4.624
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.534981 1.413819 2.50 0.013 *
## careertypeHT 1.140343 0.530928 2.15 0.032 *
## careertypeIM 0.315633 1.238087 0.25 0.799
## age -0.033080 0.019747 -1.68 0.095 .
## education 0.054364 0.042238 1.29 0.199
## sector1 -1.220342 1.107881 -1.10 0.271
## sector2 1.425429 0.954688 1.49 0.136
## sector3 -0.360151 0.381529 -0.94 0.346
## sector4 -0.557859 0.659568 -0.85 0.398
## sector5 -0.677256 0.513053 -1.32 0.188
## sector6 -0.609823 0.511725 -1.19 0.234
## sector7 1.096620 1.094452 1.00 0.317
## sector8 -0.946838 0.540102 -1.75 0.080 .
## sector9 0.840879 0.761276 1.10 0.270
## sector10 0.050332 2.619025 0.02 0.985
## sector11 -0.286651 0.423763 -0.68 0.499
## sector12 0.459175 0.491665 0.93 0.351
## sector13 0.488799 0.740248 0.66 0.509
## divorces 0.063076 0.394034 0.16 0.873
## children 0.179535 0.121589 1.48 0.141
## activecareer -0.032026 0.032018 -1.00 0.318
## voluntary -0.553154 0.504489 -1.10 0.274
## satisfaction 0.456745 0.252413 1.81 0.071 .
## disappointment 0.000536 0.173326 0.00 0.998
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.75 on 370 degrees of freedom
## (829 observations deleted due to missingness)
## Multiple R-squared: 0.0964, Adjusted R-squared: 0.0402
## F-statistic: 1.72 on 23 and 370 DF, p-value: 0.0223
Experimenting with health
- Health general (W2)
##
## Call:
## lm(formula = w2health ~ careertype + age + education + sector +
## divorces + children + activecareer + voluntary + satisfaction +
## disappointment, data = mydata, subset = gender == "male")
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3625 -0.5681 0.0447 0.6360 2.5085
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.716767 0.367071 10.13 < 2e-16 ***
## careertypeHT -0.065511 0.166302 -0.39 0.69378
## careertypeIM -0.252734 0.205647 -1.23 0.21959
## age 0.019009 0.005137 3.70 0.00024 ***
## education -0.040628 0.011928 -3.41 0.00071 ***
## sector1 0.140841 0.229456 0.61 0.53959
## sector2 0.117789 0.286551 0.41 0.68118
## sector3 0.045234 0.112370 0.40 0.68744
## sector4 -0.020541 0.221733 -0.09 0.92622
## sector5 0.083744 0.142420 0.59 0.55676
## sector6 -0.078044 0.144943 -0.54 0.59048
## sector7 -0.028639 0.297386 -0.10 0.92331
## sector8 0.225078 0.165310 1.36 0.17388
## sector9 -0.066876 0.230076 -0.29 0.77141
## sector10 -0.869507 0.917842 -0.95 0.34387
## sector11 0.094630 0.134834 0.70 0.48308
## sector12 0.140172 0.158714 0.88 0.37752
## sector13 0.163402 0.200527 0.81 0.41549
## divorces 0.045432 0.103880 0.44 0.66202
## children -0.000122 0.032286 0.00 0.99699
## activecareer -0.017597 0.007518 -2.34 0.01959 *
## voluntary -0.120351 0.141761 -0.85 0.39625
## satisfaction -0.109624 0.066974 -1.64 0.10222
## disappointment 0.176439 0.046868 3.76 0.00018 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.969 on 572 degrees of freedom
## (627 observations deleted due to missingness)
## Multiple R-squared: 0.0911, Adjusted R-squared: 0.0545
## F-statistic: 2.49 on 23 and 572 DF, p-value: 0.00016
- Long-term ilness (W2)
##
## Call:
## lm(formula = w2ilness ~ careertype + age + education + sector +
## divorces + children + activecareer + voluntary + satisfaction +
## disappointment, data = mydata, subset = gender == "male")
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.68e-15 -5.10e-17 0.00e+00 6.90e-17 7.69e-16
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.00e+00 3.56e-16 8.42e+15 <2e-16 ***
## careertypeHT 8.58e-17 1.38e-16 6.20e-01 0.535
## careertypeIM 1.21e-17 1.59e-16 8.00e-02 0.939
## age -5.73e-18 4.46e-18 -1.28e+00 0.201
## education -3.92e-19 1.06e-17 -4.00e-02 0.970
## sectorMining 5.48e-17 3.47e-16 1.60e-01 0.874
## sectorManufacturing 9.03e-17 2.21e-16 4.10e-01 0.684
## sectorUtilities 1.43e-16 2.97e-16 4.80e-01 0.629
## sectorConstruction 7.97e-17 2.28e-16 3.50e-01 0.727
## sectorWholesale&retail 5.31e-17 2.33e-16 2.30e-01 0.820
## sectorHoreca 5.70e-17 3.22e-16 1.80e-01 0.860
## sectorTransport&communication 9.41e-17 2.56e-16 3.70e-01 0.713
## sectorFinancial intermediation 3.91e-18 2.93e-16 1.00e-02 0.989
## sectorPublic administration 7.18e-17 2.27e-16 3.20e-01 0.752
## sectorEducation 5.91e-17 2.46e-16 2.40e-01 0.811
## sectorHealth and social work 3.03e-17 2.63e-16 1.20e-01 0.908
## sectorOther -6.23e-16 2.55e-16 -2.45e+00 0.015 *
## divorces -8.70e-18 8.57e-17 -1.00e-01 0.919
## children 1.63e-17 2.61e-17 6.30e-01 0.532
## activecareer -1.30e-18 5.70e-18 -2.30e-01 0.820
## voluntary 1.57e-16 1.31e-16 1.20e+00 0.230
## satisfaction 7.83e-17 5.67e-17 1.38e+00 0.169
## disappointment 8.01e-17 4.21e-17 1.90e+00 0.058 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.38e-16 on 228 degrees of freedom
## (972 observations deleted due to missingness)
## Multiple R-squared: 0.49, Adjusted R-squared: 0.441
## F-statistic: 9.95 on 22 and 228 DF, p-value: <2e-16
- Problems limiting work (W2)
##
## Call:
## lm(formula = w2worklim ~ careertype + age + education + sector +
## divorces + children + activecareer + voluntary + satisfaction +
## disappointment, data = mydata, subset = gender == "male")
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.58e-15 -6.20e-17 3.70e-17 1.66e-16 5.24e-16
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.00e+00 4.83e-16 6.21e+15 <2e-16 ***
## careertypeHT 1.08e-16 2.60e-16 4.20e-01 0.678
## careertypeIM -1.76e-16 2.34e-16 -7.50e-01 0.453
## age -8.65e-18 6.97e-18 -1.24e+00 0.217
## education -3.03e-17 1.71e-17 -1.77e+00 0.079 .
## sector1 -8.09e-18 2.92e-16 -3.00e-02 0.978
## sector2 -1.11e-16 3.31e-16 -3.40e-01 0.737
## sector3 -1.60e-16 1.40e-16 -1.14e+00 0.255
## sector4 -8.70e-17 6.11e-16 -1.40e-01 0.887
## sector5 -1.41e-17 1.75e-16 -8.00e-02 0.936
## sector6 -4.46e-17 2.01e-16 -2.20e-01 0.825
## sector7 -1.45e-16 4.40e-16 -3.30e-01 0.743
## sector8 4.78e-17 2.27e-16 2.10e-01 0.833
## sector9 8.19e-17 2.93e-16 2.80e-01 0.781
## sector10 1.87e-16 6.61e-16 2.80e-01 0.777
## sector11 1.10e-16 1.65e-16 6.60e-01 0.508
## sector12 1.31e-16 2.62e-16 5.00e-01 0.618
## sector13 -7.24e-18 2.95e-16 -2.00e-02 0.981
## divorces -5.12e-17 1.25e-16 -4.10e-01 0.684
## children 9.99e-18 4.00e-17 2.50e-01 0.803
## activecareer -9.80e-18 9.93e-18 -9.90e-01 0.326
## voluntary -2.38e-16 2.25e-16 -1.06e+00 0.292
## satisfaction -2.88e-17 8.69e-17 -3.30e-01 0.741
## disappointment -1.88e-17 7.06e-17 -2.70e-01 0.790
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.43e-16 on 116 degrees of freedom
## (1083 observations deleted due to missingness)
## Multiple R-squared: 0.515, Adjusted R-squared: 0.419
## F-statistic: 5.35 on 23 and 116 DF, p-value: 4.98e-10