So, to repeat the original article lines we need to run regressions on the following variables:
##
## Call:
## lm(formula = voluntary ~ careertype + age + education + sector +
## gender, data = data.c2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.7624 -0.1786 0.0052 0.2897 0.5288
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.151824 0.061359 -2.47 0.0135 *
## careertypeHT 0.051057 0.026705 1.91 0.0561 .
## age 0.002198 0.000889 2.47 0.0136 *
## education 0.005653 0.002438 2.32 0.0206 *
## sector1 -0.070797 0.045940 -1.54 0.1236
## sector2 -0.080394 0.068692 -1.17 0.2421
## sector3 -0.047542 0.023731 -2.00 0.0454 *
## sector4 0.003043 0.050940 0.06 0.9524
## sector5 -0.044170 0.033468 -1.32 0.1871
## sector6 -0.071733 0.026913 -2.67 0.0078 **
## sector7 0.045200 0.061071 0.74 0.4594
## sector8 0.016904 0.034720 0.49 0.6264
## sector9 0.064154 0.042291 1.52 0.1295
## sector10 0.201433 0.142689 1.41 0.1583
## sector11 0.027021 0.026096 1.04 0.3007
## sector12 -0.013175 0.028328 -0.47 0.6420
## sector13 0.006741 0.031627 0.21 0.8313
## gender1 0.031251 0.010172 3.07 0.0022 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.305 on 1279 degrees of freedom
## (559 observations deleted due to missingness)
## Multiple R-squared: 0.0357, Adjusted R-squared: 0.0229
## F-statistic: 2.79 on 17 and 1279 DF, p-value: 0.000132
##
## Call:
## lm(formula = satisfaction ~ careertype + age + education + sector +
## gender, data = data.c2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.496 -0.400 -0.189 0.583 0.998
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.53542 0.16796 -3.19 0.00147 **
## careertypeHT -0.01106 0.06953 -0.16 0.87361
## age 0.00884 0.00227 3.89 0.00011 ***
## education 0.01231 0.00574 2.15 0.03203 *
## sector1 0.15062 0.09487 1.59 0.11264
## sector2 -0.24600 0.16969 -1.45 0.14743
## sector3 -0.09797 0.05701 -1.72 0.08603 .
## sector4 0.29220 0.11702 2.50 0.01267 *
## sector5 0.07326 0.07795 0.94 0.34750
## sector6 0.00169 0.06716 0.03 0.97998
## sector7 -0.07115 0.16900 -0.42 0.67382
## sector8 0.02294 0.08830 0.26 0.79508
## sector9 0.13651 0.10426 1.31 0.19072
## sector10 -0.41961 0.42264 -0.99 0.32101
## sector11 0.06250 0.06683 0.94 0.34987
## sector12 0.13693 0.06386 2.14 0.03225 *
## sector13 0.01017 0.07877 0.13 0.89726
## gender1 0.02547 0.02330 1.09 0.27460
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.635 on 1085 degrees of freedom
## (753 observations deleted due to missingness)
## Multiple R-squared: 0.0441, Adjusted R-squared: 0.0291
## F-statistic: 2.94 on 17 and 1085 DF, p-value: 5.48e-05
##
## Call:
## lm(formula = disappointment ~ careertype + age + education +
## sector + gender, data = data.c2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.5906 -0.8408 -0.0371 0.5278 2.4727
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.92707 0.23516 3.94 8.6e-05 ***
## careertypeHT 0.17654 0.09736 1.81 0.070 .
## age -0.01339 0.00318 -4.21 2.8e-05 ***
## education 0.00980 0.00803 1.22 0.223
## sector1 -0.13735 0.13284 -1.03 0.301
## sector2 0.17887 0.23762 0.75 0.452
## sector3 -0.05547 0.07983 -0.69 0.487
## sector4 -0.10706 0.16386 -0.65 0.514
## sector5 -0.04314 0.10915 -0.40 0.693
## sector6 0.16348 0.09404 1.74 0.082 .
## sector7 0.12819 0.23666 0.54 0.588
## sector8 -0.18545 0.12365 -1.50 0.134
## sector9 0.04331 0.14600 0.30 0.767
## sector10 -0.11418 0.59182 -0.19 0.847
## sector11 0.02159 0.09337 0.23 0.817
## sector12 -0.09924 0.08943 -1.11 0.267
## sector13 -0.02251 0.11030 -0.20 0.838
## gender1 -0.00904 0.03262 -0.28 0.782
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.889 on 1086 degrees of freedom
## (752 observations deleted due to missingness)
## Multiple R-squared: 0.0371, Adjusted R-squared: 0.022
## F-statistic: 2.46 on 17 and 1086 DF, p-value: 0.000842
##
## Call:
## lm(formula = achievements ~ careertype + age + education + sector +
## gender, data = data.c2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.294 -0.331 -0.186 0.619 1.244
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.36470 0.16163 -2.26 0.0242 *
## careertypeHT -0.01126 0.06692 -0.17 0.8664
## age 0.00523 0.00219 2.39 0.0168 *
## education 0.00561 0.00552 1.02 0.3101
## sector1 0.17285 0.09130 1.89 0.0586 .
## sector2 -0.40070 0.16332 -2.45 0.0143 *
## sector3 -0.04337 0.05487 -0.79 0.4294
## sector4 0.17477 0.11262 1.55 0.1210
## sector5 0.08715 0.07502 1.16 0.2456
## sector6 -0.03271 0.06463 -0.51 0.6129
## sector7 -0.13719 0.16265 -0.84 0.3992
## sector8 0.11460 0.08499 1.35 0.1778
## sector9 0.15481 0.10035 1.54 0.1232
## sector10 -0.26517 0.40676 -0.65 0.5146
## sector11 -0.01066 0.06417 -0.17 0.8681
## sector12 0.19904 0.06163 3.23 0.0013 **
## sector13 -0.03385 0.07582 -0.45 0.6553
## gender1 0.04010 0.02246 1.79 0.0744 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.611 on 1084 degrees of freedom
## (754 observations deleted due to missingness)
## Multiple R-squared: 0.0397, Adjusted R-squared: 0.0247
## F-statistic: 2.64 on 17 and 1084 DF, p-value: 0.000314
##
## Call:
## lm(formula = sacrifices ~ careertype + age + education + sector +
## gender, data = data.c2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.596 -0.364 -0.178 0.670 1.947
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.55348 0.21452 2.58 0.010 *
## careertypeHT 0.01470 0.08871 0.17 0.868
## age -0.00714 0.00290 -2.46 0.014 *
## education 0.01635 0.00735 2.22 0.026 *
## sector1 -0.12603 0.12103 -1.04 0.298
## sector2 -0.14801 0.21648 -0.68 0.494
## sector3 -0.02130 0.07274 -0.29 0.770
## sector4 -0.01769 0.14929 -0.12 0.906
## sector5 -0.00770 0.09944 -0.08 0.938
## sector6 0.17933 0.08568 2.09 0.037 *
## sector7 0.44503 0.21561 2.06 0.039 *
## sector8 -0.11697 0.11265 -1.04 0.299
## sector9 0.15059 0.13302 1.13 0.258
## sector10 -0.22251 0.53919 -0.41 0.680
## sector11 -0.04013 0.08507 -0.47 0.637
## sector12 0.02455 0.08162 0.30 0.764
## sector13 0.09455 0.10051 0.94 0.347
## gender1 0.05313 0.02974 1.79 0.074 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.81 on 1084 degrees of freedom
## (754 observations deleted due to missingness)
## Multiple R-squared: 0.033, Adjusted R-squared: 0.0178
## F-statistic: 2.18 on 17 and 1084 DF, p-value: 0.00378
##
## Call:
## lm(formula = healthsuffered ~ careertype + age + education +
## sector + gender, data = data.c2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.623 -0.844 -0.102 0.731 2.258
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.12818 0.23707 4.76 2.2e-06 ***
## careertypeHT 0.15685 0.09809 1.60 0.110
## age -0.01586 0.00321 -4.95 8.7e-07 ***
## education -0.01393 0.00809 -1.72 0.085 .
## sector1 0.03603 0.13384 0.27 0.788
## sector2 0.35907 0.23939 1.50 0.134
## sector3 0.03316 0.08051 0.41 0.681
## sector4 -0.07846 0.16509 -0.48 0.635
## sector5 -0.04499 0.10997 -0.41 0.683
## sector6 0.13839 0.09474 1.46 0.144
## sector7 0.20313 0.23843 0.85 0.394
## sector8 -0.15020 0.12458 -1.21 0.228
## sector9 -0.12439 0.14709 -0.85 0.398
## sector10 -0.56765 0.59625 -0.95 0.341
## sector11 0.06078 0.09407 0.65 0.518
## sector12 0.09395 0.09010 1.04 0.297
## sector13 0.10566 0.11113 0.95 0.342
## gender1 -0.00274 0.03287 -0.08 0.934
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.896 on 1085 degrees of freedom
## (753 observations deleted due to missingness)
## Multiple R-squared: 0.0358, Adjusted R-squared: 0.0207
## F-statistic: 2.37 on 17 and 1085 DF, p-value: 0.00137
##
## Call:
## lm(formula = marriages ~ careertype + age + education + sector +
## gender, data = data.c2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.2798 -0.0968 -0.0520 0.0085 2.0944
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.21046 0.07205 2.92 0.0035 **
## careertypeHT 0.11346 0.03507 3.23 0.0012 **
## age -0.00333 0.00105 -3.18 0.0015 **
## education -0.00268 0.00296 -0.91 0.3654
## sector1 -0.12007 0.04934 -2.43 0.0151 *
## sector2 0.12533 0.08554 1.47 0.1431
## sector3 0.00546 0.02862 0.19 0.8487
## sector4 -0.07494 0.06442 -1.16 0.2449
## sector5 -0.02100 0.04033 -0.52 0.6026
## sector6 0.00367 0.03273 0.11 0.9107
## sector7 0.12103 0.08171 1.48 0.1387
## sector8 -0.03956 0.04449 -0.89 0.3741
## sector9 -0.09931 0.05012 -1.98 0.0477 *
## sector10 0.07055 0.15683 0.45 0.6529
## sector11 0.04313 0.03195 1.35 0.1772
## sector12 -0.04883 0.03148 -1.55 0.1211
## sector13 0.03020 0.03566 0.85 0.3972
## gender1 0.01586 0.01205 1.32 0.1882
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.412 on 1648 degrees of freedom
## (190 observations deleted due to missingness)
## Multiple R-squared: 0.0284, Adjusted R-squared: 0.0184
## F-statistic: 2.84 on 17 and 1648 DF, p-value: 9.28e-05
##
## Call:
## lm(formula = children ~ careertype + age + education + sector +
## gender, data = data.c2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.337 -0.731 -0.208 0.618 7.342
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.33320 0.22557 -5.91 4.3e-09 ***
## careertypeHT -0.16643 0.10845 -1.53 0.12508
## age 0.01911 0.00329 5.81 7.6e-09 ***
## education 0.00279 0.00909 0.31 0.75927
## sector1 0.44561 0.15453 2.88 0.00399 **
## sector2 -0.03570 0.26377 -0.14 0.89236
## sector3 -0.00662 0.08687 -0.08 0.93929
## sector4 -0.70909 0.20694 -3.43 0.00063 ***
## sector5 -0.00494 0.12520 -0.04 0.96851
## sector6 -0.04827 0.10080 -0.48 0.63214
## sector7 0.17961 0.23955 0.75 0.45351
## sector8 0.01326 0.13648 0.10 0.92260
## sector9 -0.22931 0.15659 -1.46 0.14330
## sector10 0.50065 0.49199 1.02 0.30904
## sector11 -0.17810 0.09816 -1.81 0.06982 .
## sector12 0.04826 0.09641 0.50 0.61679
## sector13 0.05873 0.10980 0.53 0.59279
## gender1 0.08459 0.03721 2.27 0.02317 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.18 on 1440 degrees of freedom
## (398 observations deleted due to missingness)
## Multiple R-squared: 0.0542, Adjusted R-squared: 0.043
## F-statistic: 4.85 on 17 and 1440 DF, p-value: 2.93e-10
##
## Call:
## lm(formula = divorces ~ careertype + age + education + sector +
## gender, data = data.c2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5331 -0.2294 -0.1497 -0.0451 2.6577
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.42705 0.07533 5.67 1.7e-08 ***
## careertypeHT 0.16638 0.03667 4.54 6.1e-06 ***
## age -0.00688 0.00110 -6.28 4.3e-10 ***
## education -0.00211 0.00310 -0.68 0.496
## sector1 -0.10094 0.05158 -1.96 0.051 .
## sector2 0.09002 0.08943 1.01 0.314
## sector3 -0.00040 0.02992 -0.01 0.989
## sector4 -0.04329 0.06735 -0.64 0.520
## sector5 0.01321 0.04216 0.31 0.754
## sector6 0.02569 0.03422 0.75 0.453
## sector7 0.12517 0.08543 1.47 0.143
## sector8 -0.06617 0.04651 -1.42 0.155
## sector9 -0.07150 0.05240 -1.36 0.173
## sector10 -0.21963 0.16397 -1.34 0.181
## sector11 0.08603 0.03341 2.58 0.010 *
## sector12 0.01539 0.03292 0.47 0.640
## sector13 0.04062 0.03729 1.09 0.276
## gender1 -0.00475 0.01260 -0.38 0.706
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.43 on 1648 degrees of freedom
## (190 observations deleted due to missingness)
## Multiple R-squared: 0.0579, Adjusted R-squared: 0.0481
## F-statistic: 5.95 on 17 and 1648 DF, p-value: 1.57e-13
##
## Call:
## lm(formula = WageMain ~ careertype + age + education + sector +
## gender, data = data.c2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1996 -464 -85 326 11646
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2254.24 320.17 7.04 4.7e-12 ***
## careertypeHT 207.61 125.29 1.66 0.0980 .
## age -11.68 4.53 -2.58 0.0101 *
## education 95.40 10.07 9.48 < 2e-16 ***
## sector1 -458.06 220.14 -2.08 0.0378 *
## sector2 183.29 330.34 0.55 0.5792
## sector3 185.57 99.93 1.86 0.0637 .
## sector4 462.49 206.98 2.23 0.0258 *
## sector5 -61.88 145.26 -0.43 0.6703
## sector6 -250.17 120.51 -2.08 0.0383 *
## sector7 -70.37 312.30 -0.23 0.8218
## sector8 261.67 160.62 1.63 0.1038
## sector9 501.57 178.41 2.81 0.0051 **
## sector10 -998.79 617.00 -1.62 0.1060
## sector11 225.53 115.20 1.96 0.0507 .
## sector12 -70.62 108.66 -0.65 0.5160
## sector13 58.96 136.67 0.43 0.6663
## gender1 246.68 44.03 5.60 3.1e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 920 on 670 degrees of freedom
## (1168 observations deleted due to missingness)
## Multiple R-squared: 0.227, Adjusted R-squared: 0.207
## F-statistic: 11.6 on 17 and 670 DF, p-value: <2e-16
Currently in deciles, convert to EUR?
##
## Call:
## lm(formula = lastsalary ~ careertype + age + education + sector +
## gender, data = data.c2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.923 -2.000 -0.076 2.062 5.646
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.8579 0.9404 4.10 4.5e-05 ***
## careertypeHT 1.2965 0.3286 3.95 8.7e-05 ***
## age -0.0548 0.0128 -4.28 2.2e-05 ***
## education 0.0949 0.0280 3.39 0.00073 ***
## sector1 -1.2809 0.8349 -1.53 0.12538
## sector2 0.8987 0.7942 1.13 0.25819
## sector3 -0.2106 0.2775 -0.76 0.44815
## sector4 -0.5536 0.5130 -1.08 0.28086
## sector5 -0.2674 0.3910 -0.68 0.49422
## sector6 0.1517 0.3482 0.44 0.66323
## sector7 1.1228 0.8830 1.27 0.20392
## sector8 -0.4366 0.4165 -1.05 0.29492
## sector9 0.3547 0.4959 0.72 0.47466
## sector10 -1.1176 1.7474 -0.64 0.52262
## sector11 0.0152 0.3056 0.05 0.96033
## sector12 0.5465 0.2946 1.86 0.06396 .
## sector13 0.5555 0.3813 1.46 0.14557
## gender1 0.5988 0.1184 5.06 5.4e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.61 on 744 degrees of freedom
## (1094 observations deleted due to missingness)
## Multiple R-squared: 0.1, Adjusted R-squared: 0.0797
## F-statistic: 4.88 on 17 and 744 DF, p-value: 4.89e-10
##
## Call:
## lm(formula = h_owner ~ careertype + age + education + sector +
## gender, data = data.c2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.515 -0.227 -0.157 -0.070 1.000
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.870981 0.068646 12.69 < 2e-16 ***
## careertypeHT 0.144285 0.033495 4.31 1.7e-05 ***
## age 0.004340 0.000999 4.35 1.5e-05 ***
## education -0.012506 0.002821 -4.43 9.9e-06 ***
## sector1 -0.027420 0.046976 -0.58 0.5595
## sector2 -0.102180 0.081446 -1.25 0.2098
## sector3 0.018248 0.027251 0.67 0.5032
## sector4 0.032576 0.061338 0.53 0.5954
## sector5 -0.021444 0.038397 -0.56 0.5766
## sector6 0.077078 0.031162 2.47 0.0135 *
## sector7 0.014036 0.077800 0.18 0.8569
## sector8 0.031350 0.042358 0.74 0.4593
## sector9 -0.041421 0.047719 -0.87 0.3855
## sector10 -0.184394 0.149329 -1.23 0.2171
## sector11 0.079326 0.030472 2.60 0.0093 **
## sector12 0.027880 0.029979 0.93 0.3525
## sector13 0.021896 0.034011 0.64 0.5198
## gender1 0.004877 0.011475 0.43 0.6709
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.392 on 1646 degrees of freedom
## (192 observations deleted due to missingness)
## Multiple R-squared: 0.0531, Adjusted R-squared: 0.0433
## F-statistic: 5.43 on 17 and 1646 DF, p-value: 5.63e-12
##
## Call:
## lm(formula = AgePension ~ careertype + age + education + sector +
## gender, data = data.c2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -35.13 -2.38 0.11 2.68 20.56
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 49.6316 1.3847 35.84 < 2e-16 ***
## careertypeHT 2.2089 0.5290 4.18 3.2e-05 ***
## age 0.1306 0.0184 7.08 2.7e-12 ***
## education 0.1501 0.0446 3.36 0.0008 ***
## sector1 3.2508 0.7330 4.43 1.0e-05 ***
## sector2 -1.7452 1.3574 -1.29 0.1988
## sector3 -1.1882 0.4440 -2.68 0.0076 **
## sector4 -1.0918 0.8970 -1.22 0.2238
## sector5 0.0571 0.6165 0.09 0.9263
## sector6 0.7387 0.5298 1.39 0.1636
## sector7 -1.0100 1.4155 -0.71 0.4757
## sector8 -0.7192 0.6929 -1.04 0.2996
## sector9 0.9283 0.8429 1.10 0.2710
## sector10 -0.6055 3.1272 -0.19 0.8465
## sector11 0.1617 0.5061 0.32 0.7495
## sector12 -1.4706 0.4820 -3.05 0.0023 **
## sector13 0.8527 0.6151 1.39 0.1660
## gender1 0.7192 0.1814 3.96 7.9e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.7 on 984 degrees of freedom
## (854 observations deleted due to missingness)
## Multiple R-squared: 0.146, Adjusted R-squared: 0.132
## F-statistic: 9.94 on 17 and 984 DF, p-value: <2e-16
This probably needs more control variables (e.g. financials)
##
## Call:
## lm(formula = activecareer ~ careertype + age + education + sector +
## gender, data = data.c2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -38.52 -2.96 0.60 3.54 19.43
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.2286 0.9495 -4.45 9.0e-06 ***
## careertypeHT 0.6498 0.4622 1.41 0.1600
## age 0.1499 0.0138 10.86 < 2e-16 ***
## education -0.2312 0.0390 -5.93 3.8e-09 ***
## sector1 3.6699 0.6501 5.64 1.9e-08 ***
## sector2 0.5071 1.1272 0.45 0.6528
## sector3 -0.6306 0.3771 -1.67 0.0947 .
## sector4 0.4374 0.8489 0.52 0.6065
## sector5 0.4226 0.5314 0.80 0.4266
## sector6 -0.6665 0.4313 -1.55 0.1224
## sector7 -1.0059 1.0767 -0.93 0.3503
## sector8 0.9569 0.5862 1.63 0.1028
## sector9 -0.0423 0.6604 -0.06 0.9490
## sector10 -3.4147 2.0667 -1.65 0.0987 .
## sector11 0.7348 0.4211 1.75 0.0812 .
## sector12 -1.2614 0.4149 -3.04 0.0024 **
## sector13 -0.5985 0.4699 -1.27 0.2030
## gender1 1.0297 0.1588 6.48 1.2e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.42 on 1648 degrees of freedom
## (190 observations deleted due to missingness)
## Multiple R-squared: 0.197, Adjusted R-squared: 0.189
## F-statistic: 23.8 on 17 and 1648 DF, p-value: <2e-16
##
## Call:
## lm(formula = LengthCareer ~ careertype + age + education + sector +
## gender, data = data.c2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -24.286 -2.632 0.261 3.164 14.313
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 36.3141 1.3861 26.20 < 2e-16 ***
## careertypeHT 2.2518 0.5295 4.25 2.3e-05 ***
## age 0.0667 0.0185 3.61 0.00032 ***
## education -0.4758 0.0447 -10.65 < 2e-16 ***
## sector1 2.7307 0.7337 3.72 0.00021 ***
## sector2 -0.4993 1.3587 -0.37 0.71332
## sector3 -0.1218 0.4445 -0.27 0.78406
## sector4 -0.3103 0.8978 -0.35 0.72971
## sector5 1.6842 0.6171 2.73 0.00646 **
## sector6 0.4090 0.5303 0.77 0.44076
## sector7 0.1694 1.4169 0.12 0.90487
## sector8 0.7173 0.6936 1.03 0.30132
## sector9 -1.7492 0.8437 -2.07 0.03841 *
## sector10 0.5841 3.1302 0.19 0.85202
## sector11 -0.4910 0.5066 -0.97 0.33270
## sector12 -2.4012 0.4825 -4.98 7.6e-07 ***
## sector13 -0.7622 0.6157 -1.24 0.21606
## gender1 0.2433 0.1816 1.34 0.18055
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.7 on 984 degrees of freedom
## (854 observations deleted due to missingness)
## Multiple R-squared: 0.274, Adjusted R-squared: 0.262
## F-statistic: 21.9 on 17 and 984 DF, p-value: <2e-16