## Warning: NAs introduced by coercion## Warning: NAs introduced by coercionAnalysis 1: Compare BMI among different social civil status after controlling for age and gender
##
## Call:
## lm(formula = BMI ~ soc_civilstatus + VisitAge + Gender, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.7186 -2.7291 -0.5832 2.0990 27.9581
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 24.609104 0.208235 118.179 < 2e-16 ***
## soc_civilstatus2 0.075695 0.082142 0.922 0.35679
## soc_civilstatus3 0.426589 0.106593 4.002 6.30e-05 ***
## soc_civilstatus4 0.051651 0.096590 0.535 0.59283
## soc_civilstatus5 -0.379644 0.134353 -2.826 0.00472 **
## soc_civilstatus6 0.133699 0.159130 0.840 0.40081
## VisitAge 0.019125 0.003368 5.679 1.38e-08 ***
## GenderMale 1.065262 0.057387 18.563 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.952 on 20032 degrees of freedom
## (503 observations deleted due to missingness)
## Multiple R-squared: 0.02093, Adjusted R-squared: 0.02059
## F-statistic: 61.17 on 7 and 20032 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = BMI ~ soc_civilstatus + VisitAge + Gender, data = data)
##
## Standardized Coefficients::
## (Intercept) soc_civilstatus2 soc_civilstatus3 soc_civilstatus4
## 0.000000000 0.006684679 0.028666543 0.003866054
## soc_civilstatus5 soc_civilstatus6 VisitAge GenderMale
## -0.020062878 0.006036648 0.040725357 0.132197187## Df Sum Sq Mean Sq F value Pr(>F)
## soc_civilstatus 5 426 85 5.454 5.10e-05 ***
## VisitAge 1 880 880 56.362 6.28e-14 ***
## Gender 1 5381 5381 344.576 < 2e-16 ***
## Residuals 20032 312812 16
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 503 observations deleted due to missingnessAnalysis 2: Compare sle_needhrs_rested (Executed by number of hours of sleep per day) among different social civil status after controlling for age and gender. 4 hours or less [1] … 10 hours or more [7]
##
## Call:
## lm(formula = sle_needhrs_rested ~ soc_civilstatus + VisitAge +
## Gender, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6717 -0.5176 -0.0766 0.5730 2.9234
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.3012814 0.0471916 112.335 <2e-16 ***
## soc_civilstatus2 -0.0101374 0.0186205 -0.544 0.586
## soc_civilstatus3 -0.0066102 0.0242368 -0.273 0.785
## soc_civilstatus4 0.0027383 0.0219323 0.125 0.901
## soc_civilstatus5 0.0121980 0.0302610 0.403 0.687
## soc_civilstatus6 -0.0042469 0.0361905 -0.117 0.907
## VisitAge -0.0123443 0.0007619 -16.203 <2e-16 ***
## GenderMale -0.2988539 0.0130099 -22.971 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.892 on 19840 degrees of freedom
## (695 observations deleted due to missingness)
## Multiple R-squared: 0.04288, Adjusted R-squared: 0.04254
## F-statistic: 127 on 7 and 19840 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = sle_needhrs_rested ~ soc_civilstatus + VisitAge +
## Gender, data = data)
##
## Standardized Coefficients::
## (Intercept) soc_civilstatus2 soc_civilstatus3 soc_civilstatus4
## 0.0000000000 -0.0039234681 -0.0019404845 0.0008967544
## soc_civilstatus5 soc_civilstatus6 VisitAge GenderMale
## 0.0028436614 -0.0008371297 -0.1153925252 -0.1625176747## Df Sum Sq Mean Sq F value Pr(>F)
## soc_civilstatus 5 14 2.8 3.544 0.00333 **
## VisitAge 1 273 273.3 343.456 < 2e-16 ***
## Gender 1 420 419.9 527.682 < 2e-16 ***
## Residuals 19840 15786 0.8
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 695 observations deleted due to missingnessAnalysis 3: Compare sle_sleephrs (Number of hours of sleep per day.) among different social civil status after controlling for age and gender. 4 hours or less [1] … 8 hours [5]
##
## Call:
## lm(formula = sle_sleephrs ~ soc_civilstatus + VisitAge + Gender,
## data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.0663 -0.8282 0.1011 0.9718 3.3382
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.3424509 0.0531225 62.920 < 2e-16 ***
## soc_civilstatus2 -0.0064461 0.0209913 -0.307 0.758783
## soc_civilstatus3 -0.1011991 0.0271642 -3.725 0.000195 ***
## soc_civilstatus4 -0.1288213 0.0246009 -5.236 1.65e-07 ***
## soc_civilstatus5 -0.0471216 0.0341955 -1.378 0.168216
## soc_civilstatus6 -0.1483138 0.0403149 -3.679 0.000235 ***
## VisitAge 0.0095240 0.0008575 11.107 < 2e-16 ***
## GenderMale -0.0270614 0.0146298 -1.850 0.064365 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.018 on 20466 degrees of freedom
## (69 observations deleted due to missingness)
## Multiple R-squared: 0.00801, Adjusted R-squared: 0.007671
## F-statistic: 23.61 on 7 and 20466 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = sle_sleephrs ~ soc_civilstatus + VisitAge + Gender,
## data = data)
##
## Standardized Coefficients::
## (Intercept) soc_civilstatus2 soc_civilstatus3 soc_civilstatus4
## 0.000000000 -0.002217868 -0.026574879 -0.037706129
## soc_civilstatus5 soc_civilstatus6 VisitAge GenderMale
## -0.009743334 -0.026324312 0.079309704 -0.013119225## Df Sum Sq Mean Sq F value Pr(>F)
## soc_civilstatus 5 43 8.57 8.258 8.35e-08 ***
## VisitAge 1 125 125.04 120.544 < 2e-16 ***
## Gender 1 4 3.55 3.422 0.0644 .
## Residuals 20466 21230 1.04
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 69 observations deleted due to missingnessAnalysis 4: Compare sle_general_gen (How do you usually sleep?) among different social civil status after controlling for age and gender. very bad [1] … very good [5]
##
## Call:
## lm(formula = sle_general_gen ~ soc_civilstatus + VisitAge + Gender,
## data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.966 -0.708 0.062 1.042 67.037
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.6438864 0.0590110 61.749 < 2e-16 ***
## soc_civilstatus2 0.0032260 0.0233180 0.138 0.8900
## soc_civilstatus3 -0.1429499 0.0301545 -4.741 2.15e-06 ***
## soc_civilstatus4 -0.1246560 0.0273080 -4.565 5.03e-06 ***
## soc_civilstatus5 -0.0385761 0.0379512 -1.016 0.3094
## soc_civilstatus6 -0.0762997 0.0447651 -1.704 0.0883 .
## VisitAge 0.0009745 0.0009525 1.023 0.3063
## GenderMale 0.2484590 0.0162519 15.288 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.133 on 20515 degrees of freedom
## (20 observations deleted due to missingness)
## Multiple R-squared: 0.01522, Adjusted R-squared: 0.01489
## F-statistic: 45.31 on 7 and 20515 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = sle_general_gen ~ soc_civilstatus + VisitAge + Gender,
## data = data)
##
## Standardized Coefficients::
## (Intercept) soc_civilstatus2 soc_civilstatus3 soc_civilstatus4
## 0.0000000000 0.0009944946 -0.0336546137 -0.0327138166
## soc_civilstatus5 soc_civilstatus6 VisitAge GenderMale
## -0.0071526703 -0.0121365453 0.0072699860 0.1079035511## Df Sum Sq Mean Sq F value Pr(>F)
## soc_civilstatus 5 99 19.78 15.421 3.62e-15 ***
## VisitAge 1 8 8.10 6.317 0.012 *
## Gender 1 300 299.81 233.723 < 2e-16 ***
## Residuals 20515 26316 1.28
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 20 observations deleted due to missingnessAnalysis 5: Compare alc_drink_frq among different social civil status after controlling for age and gender. 4 times a week or more often [1]… Never [6]
##
## Call:
## lm(formula = alc_drink_frq ~ soc_civilstatus + VisitAge + Gender,
## data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9416 -1.0424 -0.0872 1.0140 3.2934
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.791183 0.070450 53.814 < 2e-16 ***
## soc_civilstatus2 0.011284 0.027695 0.407 0.684
## soc_civilstatus3 0.675669 0.036037 18.749 < 2e-16 ***
## soc_civilstatus4 0.413629 0.032589 12.692 < 2e-16 ***
## soc_civilstatus5 -0.012872 0.045023 -0.286 0.775
## soc_civilstatus6 0.431926 0.053723 8.040 9.49e-16 ***
## VisitAge -0.011176 0.001137 -9.830 < 2e-16 ***
## GenderMale -0.235229 0.019367 -12.146 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.337 on 20106 degrees of freedom
## (429 observations deleted due to missingness)
## Multiple R-squared: 0.04142, Adjusted R-squared: 0.04108
## F-statistic: 124.1 on 7 and 20106 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = alc_drink_frq ~ soc_civilstatus + VisitAge + Gender,
## data = data)
##
## Standardized Coefficients::
## (Intercept) soc_civilstatus2 soc_civilstatus3 soc_civilstatus4
## 0.000000000 0.002920223 0.132678748 0.090663353
## soc_civilstatus5 soc_civilstatus6 VisitAge GenderMale
## -0.002005548 0.057030165 -0.069602508 -0.085448033## Df Sum Sq Mean Sq F value Pr(>F)
## soc_civilstatus 5 1070 214.07 119.8 <2e-16 ***
## VisitAge 1 218 218.15 122.1 <2e-16 ***
## Gender 1 264 263.54 147.5 <2e-16 ***
## Residuals 20106 35920 1.79
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 429 observations deleted due to missingnessAnalysis 6: Compare alc_drink_summ among different social civil status after controlling for age and gender. 1 to 2 glasses [1] …10 glasses or more [5]
##
## Call:
## lm(formula = alc_drink_summ ~ soc_civilstatus + VisitAge + Gender,
## data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8552 -0.4057 -0.2040 0.4308 3.6147
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.8168924 0.0322414 56.353 < 2e-16 ***
## soc_civilstatus2 0.0352048 0.0125971 2.795 0.0052 **
## soc_civilstatus3 0.1265463 0.0168355 7.517 5.87e-14 ***
## soc_civilstatus4 0.0832261 0.0149707 5.559 2.74e-08 ***
## soc_civilstatus5 0.0919403 0.0205528 4.473 7.74e-06 ***
## soc_civilstatus6 0.0170238 0.0247574 0.688 0.4917
## VisitAge -0.0102155 0.0005209 -19.611 < 2e-16 ***
## GenderMale 0.3345495 0.0088669 37.730 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6001 on 19337 degrees of freedom
## (1198 observations deleted due to missingness)
## Multiple R-squared: 0.08425, Adjusted R-squared: 0.08392
## F-statistic: 254.1 on 7 and 19337 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = alc_drink_summ ~ soc_civilstatus + VisitAge + Gender,
## data = data)
##
## Standardized Coefficients::
## (Intercept) soc_civilstatus2 soc_civilstatus3 soc_civilstatus4
## 0.000000000 0.019956630 0.052979985 0.039552527
## soc_civilstatus5 soc_civilstatus6 VisitAge GenderMale
## 0.031274661 0.004860106 -0.138466982 0.264593990## Df Sum Sq Mean Sq F value Pr(>F)
## soc_civilstatus 5 37 7.3 20.38 <2e-16 ***
## VisitAge 1 91 91.3 253.60 <2e-16 ***
## Gender 1 513 512.6 1423.55 <2e-16 ***
## Residuals 19337 6963 0.4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 1198 observations deleted due to missingnessAnalysis 7: Compare scr_sc23 (Lost interest> 2 weeks.) among different social civil status after controlling for age and gender. yes [1] no [0]
##
## Call:
## glm(formula = scr_sc23 ~ soc_civilstatus + VisitAge + Gender,
## family = binomial, data = data)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.4873 -0.9566 -0.7485 1.2381 1.8634
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.226357 0.110890 11.059 < 2e-16 ***
## soc_civilstatus2 0.318870 0.043344 7.357 1.89e-13 ***
## soc_civilstatus3 0.688281 0.055034 12.506 < 2e-16 ***
## soc_civilstatus4 0.727522 0.049784 14.613 < 2e-16 ***
## soc_civilstatus5 0.610356 0.069012 8.844 < 2e-16 ***
## soc_civilstatus6 0.835612 0.081184 10.293 < 2e-16 ***
## VisitAge -0.029513 0.001807 -16.335 < 2e-16 ***
## GenderMale -0.496119 0.031005 -16.001 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 26696 on 20231 degrees of freedom
## Residual deviance: 25580 on 20224 degrees of freedom
## (311 observations deleted due to missingness)
## AIC: 25596
##
## Number of Fisher Scoring iterations: 4##
## Call:
## glm(formula = scr_sc23 ~ soc_civilstatus + VisitAge + Gender,
## family = binomial, data = data)
##
## Standardized Coefficients::
## (Intercept) soc_civilstatus2 soc_civilstatus3 soc_civilstatus4
## 0.0000000 0.2323992 0.3810884 0.4498753
## soc_civilstatus5 soc_civilstatus6 VisitAge GenderMale
## 0.2673434 0.3132077 -0.5195312 -0.5088141## Df Sum Sq Mean Sq F value Pr(>F)
## soc_civilstatus 5 127 25.31 114.6 <2e-16 ***
## VisitAge 1 73 72.75 329.3 <2e-16 ***
## Gender 1 57 57.04 258.2 <2e-16 ***
## Residuals 20224 4468 0.22
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 311 observations deleted due to missingnessAnalysis 8 : Compare sle_sleep_qual among different social civil status after controlling for age and gender. never or rarely[4] … 4 times or more a week [1]
## [1] "sle_sleep_qual_brea"
##
## Call:
## lm(formula = data[, sle_qual_names[i]] ~ data$soc_civilstatus +
## data$VisitAge + data$Gender)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9000 0.1396 0.2320 0.4101 2.1774
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.1700129 0.0438235 95.155 < 2e-16 ***
## data$soc_civilstatus2 -0.0095807 0.0171870 -0.557 0.577234
## data$soc_civilstatus3 -0.0023276 0.0239823 -0.097 0.922683
## data$soc_civilstatus4 -0.0784458 0.0213699 -3.671 0.000242 ***
## data$soc_civilstatus5 0.0006517 0.0280532 0.023 0.981466
## data$soc_civilstatus6 -0.0549738 0.0358657 -1.533 0.125351
## data$VisitAge -0.0060009 0.0007101 -8.451 < 2e-16 ***
## data$GenderMale -0.2440156 0.0121200 -20.133 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7878 on 17765 degrees of freedom
## (2770 observations deleted due to missingness)
## Multiple R-squared: 0.02888, Adjusted R-squared: 0.02849
## F-statistic: 75.47 on 7 and 17765 DF, p-value: < 2.2e-16
##
## Df Sum Sq Mean Sq F value Pr(>F)
## data$soc_civilstatus 5 5 0.95 1.538 0.174
## data$VisitAge 1 72 71.51 115.219 <2e-16 ***
## data$Gender 1 252 251.58 405.351 <2e-16 ***
## Residuals 17765 11026 0.62
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 2770 observations deleted due to missingness
## [1] "sle_sleep_qual_daya"
##
## Call:
## lm(formula = data[, sle_qual_names[i]] ~ data$soc_civilstatus +
## data$VisitAge + data$Gender)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.6863 -0.5019 0.3909 0.4749 0.6570
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.8599792 0.0399154 96.704 < 2e-16 ***
## data$soc_civilstatus2 -0.0044647 0.0157615 -0.283 0.77697
## data$soc_civilstatus3 -0.1063028 0.0204402 -5.201 2.00e-07 ***
## data$soc_civilstatus4 -0.1226080 0.0185064 -6.625 3.55e-11 ***
## data$soc_civilstatus5 -0.0783905 0.0256681 -3.054 0.00226 **
## data$soc_civilstatus6 -0.0517674 0.0303053 -1.708 0.08762 .
## data$VisitAge -0.0038598 0.0006445 -5.989 2.15e-09 ***
## data$GenderMale -0.1010643 0.0109885 -9.197 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7641 on 20410 degrees of freedom
## (125 observations deleted due to missingness)
## Multiple R-squared: 0.008917, Adjusted R-squared: 0.008577
## F-statistic: 26.23 on 7 and 20410 DF, p-value: < 2.2e-16
##
## Df Sum Sq Mean Sq F value Pr(>F)
## data$soc_civilstatus 5 30 5.98 10.23 8.18e-10 ***
## data$VisitAge 1 28 27.95 47.87 4.69e-12 ***
## data$Gender 1 49 49.39 84.59 < 2e-16 ***
## Residuals 20410 11916 0.58
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 125 observations deleted due to missingness
## [1] "sle_sleep_qual_dayt"
##
## Call:
## lm(formula = data[, sle_qual_names[i]] ~ data$soc_civilstatus +
## data$VisitAge + data$Gender)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4411 -0.3745 0.5746 0.7476 1.0546
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.9981729 0.0487137 61.547 < 2e-16 ***
## data$soc_civilstatus2 -0.0431137 0.0192236 -2.243 0.024924 *
## data$soc_civilstatus3 -0.2287600 0.0249847 -9.156 < 2e-16 ***
## data$soc_civilstatus4 -0.1727568 0.0225572 -7.659 1.96e-14 ***
## data$soc_civilstatus5 -0.1204336 0.0313350 -3.843 0.000122 ***
## data$soc_civilstatus6 -0.0969685 0.0369598 -2.624 0.008707 **
## data$VisitAge 0.0039119 0.0007865 4.974 6.61e-07 ***
## data$GenderMale 0.1455748 0.0134154 10.851 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9318 on 20367 degrees of freedom
## (168 observations deleted due to missingness)
## Multiple R-squared: 0.01603, Adjusted R-squared: 0.01569
## F-statistic: 47.39 on 7 and 20367 DF, p-value: < 2.2e-16
##
## Df Sum Sq Mean Sq F value Pr(>F)
## data$soc_civilstatus 5 154 30.81 35.49 < 2e-16 ***
## data$VisitAge 1 32 31.71 36.53 1.53e-09 ***
## data$Gender 1 102 102.23 117.75 < 2e-16 ***
## Residuals 20367 17682 0.87
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 168 observations deleted due to missingness
## [1] "sle_sleep_qual_earl"
##
## Call:
## lm(formula = data[, sle_qual_names[i]] ~ data$soc_civilstatus +
## data$VisitAge + data$Gender)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.06016 -0.94657 -0.03466 0.95609 1.11531
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.0772253 0.0534032 57.622 < 2e-16 ***
## data$soc_civilstatus2 0.0017616 0.0210896 0.084 0.93343
## data$soc_civilstatus3 -0.1456502 0.0273373 -5.328 1.00e-07 ***
## data$soc_civilstatus4 -0.1447739 0.0247749 -5.844 5.19e-09 ***
## data$soc_civilstatus5 -0.0984183 0.0343495 -2.865 0.00417 **
## data$soc_civilstatus6 -0.0840485 0.0407628 -2.062 0.03923 *
## data$VisitAge -0.0006169 0.0008627 -0.715 0.47457
## data$GenderMale 0.0089360 0.0147141 0.607 0.54366
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.019 on 20263 degrees of freedom
## (272 observations deleted due to missingness)
## Multiple R-squared: 0.003326, Adjusted R-squared: 0.002982
## F-statistic: 9.66 on 7 and 20263 DF, p-value: 4.688e-12
##
## Df Sum Sq Mean Sq F value Pr(>F)
## data$soc_civilstatus 5 69 13.883 13.363 4.95e-13 ***
## data$VisitAge 1 0 0.451 0.434 0.510
## data$Gender 1 0 0.383 0.369 0.544
## Residuals 20263 21051 1.039
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 272 observations deleted due to missingness
## [1] "sle_sleep_qual_hard"
##
## Call:
## lm(formula = data[, sle_qual_names[i]] ~ data$soc_civilstatus +
## data$VisitAge + data$Gender)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4870 -0.4148 0.0510 0.7622 1.2570
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.5675606 0.0482122 73.997 < 2e-16 ***
## data$soc_civilstatus2 0.0055868 0.0190423 0.293 0.76923
## data$soc_civilstatus3 -0.1360880 0.0246728 -5.516 3.52e-08 ***
## data$soc_civilstatus4 -0.1387014 0.0223521 -6.205 5.56e-10 ***
## data$soc_civilstatus5 -0.0680497 0.0310075 -2.195 0.02820 *
## data$soc_civilstatus6 -0.1150509 0.0366451 -3.140 0.00169 **
## data$VisitAge -0.0090247 0.0007783 -11.595 < 2e-16 ***
## data$GenderMale 0.3199787 0.0132786 24.097 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9235 on 20428 degrees of freedom
## (107 observations deleted due to missingness)
## Multiple R-squared: 0.03946, Adjusted R-squared: 0.03913
## F-statistic: 119.9 on 7 and 20428 DF, p-value: < 2.2e-16
##
## Df Sum Sq Mean Sq F value Pr(>F)
## data$soc_civilstatus 5 147 29.3 34.41 <2e-16 ***
## data$VisitAge 1 74 73.8 86.54 <2e-16 ***
## data$Gender 1 495 495.2 580.68 <2e-16 ***
## Residuals 20428 17421 0.9
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 107 observations deleted due to missingness
## [1] "sle_sleep_qual_legs"
##
## Call:
## lm(formula = data[, sle_qual_names[i]] ~ data$soc_civilstatus +
## data$VisitAge + data$Gender)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.7501 0.2716 0.3130 0.4278 0.5065
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.6630591 0.0408212 89.734 <2e-16 ***
## data$soc_civilstatus2 -0.0281997 0.0161320 -1.748 0.0805 .
## data$soc_civilstatus3 0.0231686 0.0208703 1.110 0.2670
## data$soc_civilstatus4 -0.0581026 0.0189710 -3.063 0.0022 **
## data$soc_civilstatus5 -0.0384732 0.0263079 -1.462 0.1436
## data$soc_civilstatus6 0.0023177 0.0310747 0.075 0.9405
## data$VisitAge -0.0014661 0.0006589 -2.225 0.0261 *
## data$GenderMale 0.1312888 0.0112514 11.669 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7791 on 20262 degrees of freedom
## (273 observations deleted due to missingness)
## Multiple R-squared: 0.008064, Adjusted R-squared: 0.007721
## F-statistic: 23.53 on 7 and 20262 DF, p-value: < 2.2e-16
##
## Df Sum Sq Mean Sq F value Pr(>F)
## data$soc_civilstatus 5 17 3.32 5.475 4.86e-05 ***
## data$VisitAge 1 1 0.72 1.191 0.275
## data$Gender 1 83 82.66 136.158 < 2e-16 ***
## Residuals 20262 12300 0.61
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 273 observations deleted due to missingness
## [1] "sle_sleep_qual_notr"
##
## Call:
## lm(formula = data[, sle_qual_names[i]] ~ data$soc_civilstatus +
## data$VisitAge + data$Gender)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3135 -0.7397 0.1369 0.9099 1.7377
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.262924 0.052714 23.958 < 2e-16 ***
## data$soc_civilstatus2 -0.028702 0.020816 -1.379 0.16795
## data$soc_civilstatus3 -0.118842 0.026971 -4.406 1.06e-05 ***
## data$soc_civilstatus4 -0.151991 0.024431 -6.221 5.03e-10 ***
## data$soc_civilstatus5 -0.075932 0.033998 -2.233 0.02553 *
## data$soc_civilstatus6 -0.115439 0.040354 -2.861 0.00423 **
## data$VisitAge 0.025031 0.000852 29.380 < 2e-16 ***
## data$GenderMale 0.148300 0.014529 10.207 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.004 on 20168 degrees of freedom
## (367 observations deleted due to missingness)
## Multiple R-squared: 0.05383, Adjusted R-squared: 0.0535
## F-statistic: 163.9 on 7 and 20168 DF, p-value: < 2.2e-16
##
## Df Sum Sq Mean Sq F value Pr(>F)
## data$soc_civilstatus 5 112 22.4 22.23 <2e-16 ***
## data$VisitAge 1 940 939.9 932.00 <2e-16 ***
## data$Gender 1 105 105.1 104.19 <2e-16 ***
## Residuals 20168 20339 1.0
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 367 observations deleted due to missingness
## [1] "sle_sleep_qual_seve"
##
## Call:
## lm(formula = data[, sle_qual_names[i]] ~ data$soc_civilstatus +
## data$VisitAge + data$Gender)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.35001 -0.79067 0.07215 0.85758 1.26065
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.4582894 0.0494827 69.889 <2e-16 ***
## data$soc_civilstatus2 0.0313347 0.0195549 1.602 0.109
## data$soc_civilstatus3 0.0155356 0.0252933 0.614 0.539
## data$soc_civilstatus4 0.0174299 0.0229243 0.760 0.447
## data$soc_civilstatus5 0.0040851 0.0318342 0.128 0.898
## data$soc_civilstatus6 -0.0238793 0.0376895 -0.634 0.526
## data$VisitAge -0.0091455 0.0007988 -11.449 <2e-16 ***
## data$GenderMale 0.2877304 0.0136256 21.117 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9472 on 20403 degrees of freedom
## (132 observations deleted due to missingness)
## Multiple R-squared: 0.02708, Adjusted R-squared: 0.02674
## F-statistic: 81.12 on 7 and 20403 DF, p-value: < 2.2e-16
##
## Df Sum Sq Mean Sq F value Pr(>F)
## data$soc_civilstatus 5 29 5.8 6.511 4.67e-06 ***
## data$VisitAge 1 80 80.1 89.331 < 2e-16 ***
## data$Gender 1 400 400.0 445.922 < 2e-16 ***
## Residuals 20403 18304 0.9
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 132 observations deleted due to missingness
## [1] "sle_sleep_qual_shor"
##
## Call:
## lm(formula = data[, sle_qual_names[i]] ~ data$soc_civilstatus +
## data$VisitAge + data$Gender)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3154 -0.6427 0.1062 0.7410 1.8689
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.0170207 0.0491508 20.692 < 2e-16 ***
## data$soc_civilstatus2 0.0011723 0.0193445 0.061 0.951679
## data$soc_civilstatus3 -0.0703476 0.0252795 -2.783 0.005394 **
## data$soc_civilstatus4 -0.1472450 0.0227938 -6.460 1.07e-10 ***
## data$soc_civilstatus5 -0.1033559 0.0315377 -3.277 0.001050 **
## data$soc_civilstatus6 -0.1243226 0.0375051 -3.315 0.000919 ***
## data$VisitAge 0.0280284 0.0007947 35.270 < 2e-16 ***
## data$GenderMale 0.1670429 0.0135384 12.338 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9316 on 19984 degrees of freedom
## (551 observations deleted due to missingness)
## Multiple R-squared: 0.07468, Adjusted R-squared: 0.07435
## F-statistic: 230.4 on 7 and 19984 DF, p-value: < 2.2e-16
##
## Df Sum Sq Mean Sq F value Pr(>F)
## data$soc_civilstatus 5 99 19.9 22.91 <2e-16 ***
## data$VisitAge 1 1168 1168.1 1345.97 <2e-16 ***
## data$Gender 1 132 132.1 152.24 <2e-16 ***
## Residuals 19984 17343 0.9
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 551 observations deleted due to missingness
## [1] "sle_sleep_qual_snor"
##
## Call:
## lm(formula = data[, sle_qual_names[i]] ~ data$soc_civilstatus +
## data$VisitAge + data$Gender)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2946 -0.9518 0.2491 1.0017 1.5748
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.528202 0.061810 57.082 < 2e-16 ***
## data$soc_civilstatus2 0.016421 0.023974 0.685 0.493393
## data$soc_civilstatus3 0.194049 0.034207 5.673 1.42e-08 ***
## data$soc_civilstatus4 0.030868 0.030270 1.020 0.307851
## data$soc_civilstatus5 0.130472 0.039615 3.293 0.000991 ***
## data$soc_civilstatus6 0.038435 0.050819 0.756 0.449476
## data$VisitAge -0.009297 0.001002 -9.281 < 2e-16 ***
## data$GenderMale -0.396408 0.017027 -23.281 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.141 on 18901 degrees of freedom
## (1634 observations deleted due to missingness)
## Multiple R-squared: 0.03929, Adjusted R-squared: 0.03893
## F-statistic: 110.4 on 7 and 18901 DF, p-value: < 2.2e-16
##
## Df Sum Sq Mean Sq F value Pr(>F)
## data$soc_civilstatus 5 116 23.1 17.76 <2e-16 ***
## data$VisitAge 1 185 185.2 142.11 <2e-16 ***
## data$Gender 1 706 706.1 541.99 <2e-16 ***
## Residuals 18901 24626 1.3
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 1634 observations deleted due to missingness
## [1] "sle_sleep_qual_worr"
##
## Call:
## lm(formula = data[, sle_qual_names[i]] ~ data$soc_civilstatus +
## data$VisitAge + data$Gender)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1915 -0.8117 0.1594 0.9126 1.3095
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.4706946 0.0537858 45.936 < 2e-16 ***
## data$soc_civilstatus2 -0.0025420 0.0212439 -0.120 0.905
## data$soc_civilstatus3 0.0013826 0.0276905 0.050 0.960
## data$soc_civilstatus4 -0.0400920 0.0249721 -1.605 0.108
## data$soc_civilstatus5 -0.0289146 0.0345822 -0.836 0.403
## data$soc_civilstatus6 0.0120580 0.0411580 0.293 0.770
## data$VisitAge 0.0057749 0.0008692 6.644 3.13e-11 ***
## data$GenderMale 0.2814180 0.0148385 18.965 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.024 on 20127 degrees of freedom
## (408 observations deleted due to missingness)
## Multiple R-squared: 0.02205, Adjusted R-squared: 0.02171
## F-statistic: 64.84 on 7 and 20127 DF, p-value: < 2.2e-16
##
## Df Sum Sq Mean Sq F value Pr(>F)
## data$soc_civilstatus 5 22 4.4 4.186 0.000838 ***
## data$VisitAge 1 77 76.8 73.252 < 2e-16 ***
## data$Gender 1 377 377.1 359.687 < 2e-16 ***
## Residuals 20127 21102 1.0
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 408 observations deleted due to missingnessAnalysis 9 : Compare die_brea_dummy among different social civil status after controlling for age and gender. Note: for Die_meal, there is no such variables in the dataset
##
## Call:
## glm(formula = die_brea_dummy ~ soc_civilstatus + VisitAge + Gender,
## family = binomial, data = data)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.8076 0.2796 0.3537 0.4749 0.9057
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.800515 0.184861 -9.740 < 2e-16 ***
## soc_civilstatus2 0.079224 0.074230 1.067 0.2858
## soc_civilstatus3 -0.029718 0.093154 -0.319 0.7497
## soc_civilstatus4 -0.172371 0.083164 -2.073 0.0382 *
## soc_civilstatus5 -0.105063 0.114679 -0.916 0.3596
## soc_civilstatus6 -0.194919 0.161993 -1.203 0.2289
## VisitAge 0.068879 0.003184 21.632 < 2e-16 ***
## GenderMale 0.408265 0.054645 7.471 7.95e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 11877 on 20522 degrees of freedom
## Residual deviance: 11272 on 20515 degrees of freedom
## (20 observations deleted due to missingness)
## AIC: 11288
##
## Number of Fisher Scoring iterations: 5##
## Call:
## glm(formula = die_brea_dummy ~ soc_civilstatus + VisitAge + Gender,
## family = binomial, data = data)
##
## Standardized Coefficients::
## (Intercept) soc_civilstatus2 soc_civilstatus3 soc_civilstatus4
## 0.00000000 0.10025419 -0.02872036 -0.18569251
## soc_civilstatus5 soc_civilstatus6 VisitAge GenderMale
## -0.07996718 -0.12727370 2.10935451 0.72784079## Df Sum Sq Mean Sq F value Pr(>F)
## soc_civilstatus 5 1.4 0.29 3.821 0.00184 **
## VisitAge 1 40.9 40.86 544.503 < 2e-16 ***
## Gender 1 4.0 3.98 52.969 3.51e-13 ***
## Residuals 20515 1539.6 0.08
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 20 observations deleted due to missingnessAnalysis 10: Compare qua_stress among different social civil status after controlling for age and gender. Never [5] … Very often [1]
## Warning: NAs introduced by coercion## [1] "qua_stress_1"
##
## Call:
## lm(formula = data$qua_stress_1 ~ data$soc_civilstatus + data$VisitAge +
## data$Gender)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.05 -7.08 -5.91 -4.98 990.47
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.28526 4.24881 1.009 0.3132
## data$soc_civilstatus2 1.08700 1.67901 0.647 0.5174
## data$soc_civilstatus3 9.52186 2.17091 4.386 1.16e-05 ***
## data$soc_civilstatus4 0.52700 1.96599 0.268 0.7887
## data$soc_civilstatus5 4.00461 2.73222 1.466 0.1427
## data$soc_civilstatus6 5.73127 3.22279 1.778 0.0754 .
## data$VisitAge 0.06895 0.06858 1.005 0.3147
## data$GenderMale 1.03908 1.17006 0.888 0.3745
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 81.54 on 20514 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.001168, Adjusted R-squared: 0.0008269
## F-statistic: 3.426 on 7 and 20514 DF, p-value: 0.001152
##
## Df Sum Sq Mean Sq F value Pr(>F)
## data$soc_civilstatus 5 146216 29243 4.398 0.000528 ***
## data$VisitAge 1 7994 7994 1.202 0.272856
## data$Gender 1 5243 5243 0.789 0.374519
## Residuals 20514 136389466 6649
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 21 observations deleted due to missingness## Warning: NAs introduced by coercion## [1] "qua_stress_2"
##
## Call:
## lm(formula = data$qua_stress_1 ~ data$soc_civilstatus + data$VisitAge +
## data$Gender)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.05 -7.08 -5.91 -4.98 990.47
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.28526 4.24881 1.009 0.3132
## data$soc_civilstatus2 1.08700 1.67901 0.647 0.5174
## data$soc_civilstatus3 9.52186 2.17091 4.386 1.16e-05 ***
## data$soc_civilstatus4 0.52700 1.96599 0.268 0.7887
## data$soc_civilstatus5 4.00461 2.73222 1.466 0.1427
## data$soc_civilstatus6 5.73127 3.22279 1.778 0.0754 .
## data$VisitAge 0.06895 0.06858 1.005 0.3147
## data$GenderMale 1.03908 1.17006 0.888 0.3745
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 81.54 on 20514 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.001168, Adjusted R-squared: 0.0008269
## F-statistic: 3.426 on 7 and 20514 DF, p-value: 0.001152
##
## Df Sum Sq Mean Sq F value Pr(>F)
## data$soc_civilstatus 5 146216 29243 4.398 0.000528 ***
## data$VisitAge 1 7994 7994 1.202 0.272856
## data$Gender 1 5243 5243 0.789 0.374519
## Residuals 20514 136389466 6649
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 21 observations deleted due to missingness## Warning: NAs introduced by coercion## [1] "qua_stress_3"
##
## Call:
## lm(formula = data$qua_stress_1 ~ data$soc_civilstatus + data$VisitAge +
## data$Gender)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.05 -7.08 -5.91 -4.98 990.47
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.28526 4.24881 1.009 0.3132
## data$soc_civilstatus2 1.08700 1.67901 0.647 0.5174
## data$soc_civilstatus3 9.52186 2.17091 4.386 1.16e-05 ***
## data$soc_civilstatus4 0.52700 1.96599 0.268 0.7887
## data$soc_civilstatus5 4.00461 2.73222 1.466 0.1427
## data$soc_civilstatus6 5.73127 3.22279 1.778 0.0754 .
## data$VisitAge 0.06895 0.06858 1.005 0.3147
## data$GenderMale 1.03908 1.17006 0.888 0.3745
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 81.54 on 20514 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.001168, Adjusted R-squared: 0.0008269
## F-statistic: 3.426 on 7 and 20514 DF, p-value: 0.001152
##
## Df Sum Sq Mean Sq F value Pr(>F)
## data$soc_civilstatus 5 146216 29243 4.398 0.000528 ***
## data$VisitAge 1 7994 7994 1.202 0.272856
## data$Gender 1 5243 5243 0.789 0.374519
## Residuals 20514 136389466 6649
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 21 observations deleted due to missingness## Warning: NAs introduced by coercion## [1] "qua_stress_4"
##
## Call:
## lm(formula = data$qua_stress_1 ~ data$soc_civilstatus + data$VisitAge +
## data$Gender)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.05 -7.08 -5.91 -4.98 990.47
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.28526 4.24881 1.009 0.3132
## data$soc_civilstatus2 1.08700 1.67901 0.647 0.5174
## data$soc_civilstatus3 9.52186 2.17091 4.386 1.16e-05 ***
## data$soc_civilstatus4 0.52700 1.96599 0.268 0.7887
## data$soc_civilstatus5 4.00461 2.73222 1.466 0.1427
## data$soc_civilstatus6 5.73127 3.22279 1.778 0.0754 .
## data$VisitAge 0.06895 0.06858 1.005 0.3147
## data$GenderMale 1.03908 1.17006 0.888 0.3745
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 81.54 on 20514 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.001168, Adjusted R-squared: 0.0008269
## F-statistic: 3.426 on 7 and 20514 DF, p-value: 0.001152
##
## Df Sum Sq Mean Sq F value Pr(>F)
## data$soc_civilstatus 5 146216 29243 4.398 0.000528 ***
## data$VisitAge 1 7994 7994 1.202 0.272856
## data$Gender 1 5243 5243 0.789 0.374519
## Residuals 20514 136389466 6649
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 21 observations deleted due to missingness## Warning: NAs introduced by coercion## [1] "qua_stress_5"
##
## Call:
## lm(formula = data$qua_stress_1 ~ data$soc_civilstatus + data$VisitAge +
## data$Gender)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.05 -7.08 -5.91 -4.98 990.47
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.28526 4.24881 1.009 0.3132
## data$soc_civilstatus2 1.08700 1.67901 0.647 0.5174
## data$soc_civilstatus3 9.52186 2.17091 4.386 1.16e-05 ***
## data$soc_civilstatus4 0.52700 1.96599 0.268 0.7887
## data$soc_civilstatus5 4.00461 2.73222 1.466 0.1427
## data$soc_civilstatus6 5.73127 3.22279 1.778 0.0754 .
## data$VisitAge 0.06895 0.06858 1.005 0.3147
## data$GenderMale 1.03908 1.17006 0.888 0.3745
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 81.54 on 20514 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.001168, Adjusted R-squared: 0.0008269
## F-statistic: 3.426 on 7 and 20514 DF, p-value: 0.001152
##
## Df Sum Sq Mean Sq F value Pr(>F)
## data$soc_civilstatus 5 146216 29243 4.398 0.000528 ***
## data$VisitAge 1 7994 7994 1.202 0.272856
## data$Gender 1 5243 5243 0.789 0.374519
## Residuals 20514 136389466 6649
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 21 observations deleted due to missingness## Warning: NAs introduced by coercion## [1] "qua_stress_6"
##
## Call:
## lm(formula = data$qua_stress_1 ~ data$soc_civilstatus + data$VisitAge +
## data$Gender)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.05 -7.08 -5.91 -4.98 990.47
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.28526 4.24881 1.009 0.3132
## data$soc_civilstatus2 1.08700 1.67901 0.647 0.5174
## data$soc_civilstatus3 9.52186 2.17091 4.386 1.16e-05 ***
## data$soc_civilstatus4 0.52700 1.96599 0.268 0.7887
## data$soc_civilstatus5 4.00461 2.73222 1.466 0.1427
## data$soc_civilstatus6 5.73127 3.22279 1.778 0.0754 .
## data$VisitAge 0.06895 0.06858 1.005 0.3147
## data$GenderMale 1.03908 1.17006 0.888 0.3745
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 81.54 on 20514 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.001168, Adjusted R-squared: 0.0008269
## F-statistic: 3.426 on 7 and 20514 DF, p-value: 0.001152
##
## Df Sum Sq Mean Sq F value Pr(>F)
## data$soc_civilstatus 5 146216 29243 4.398 0.000528 ***
## data$VisitAge 1 7994 7994 1.202 0.272856
## data$Gender 1 5243 5243 0.789 0.374519
## Residuals 20514 136389466 6649
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 21 observations deleted due to missingness## Warning: NAs introduced by coercion## [1] "qua_stress_7"
##
## Call:
## lm(formula = data$qua_stress_1 ~ data$soc_civilstatus + data$VisitAge +
## data$Gender)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.05 -7.08 -5.91 -4.98 990.47
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.28526 4.24881 1.009 0.3132
## data$soc_civilstatus2 1.08700 1.67901 0.647 0.5174
## data$soc_civilstatus3 9.52186 2.17091 4.386 1.16e-05 ***
## data$soc_civilstatus4 0.52700 1.96599 0.268 0.7887
## data$soc_civilstatus5 4.00461 2.73222 1.466 0.1427
## data$soc_civilstatus6 5.73127 3.22279 1.778 0.0754 .
## data$VisitAge 0.06895 0.06858 1.005 0.3147
## data$GenderMale 1.03908 1.17006 0.888 0.3745
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 81.54 on 20514 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.001168, Adjusted R-squared: 0.0008269
## F-statistic: 3.426 on 7 and 20514 DF, p-value: 0.001152
##
## Df Sum Sq Mean Sq F value Pr(>F)
## data$soc_civilstatus 5 146216 29243 4.398 0.000528 ***
## data$VisitAge 1 7994 7994 1.202 0.272856
## data$Gender 1 5243 5243 0.789 0.374519
## Residuals 20514 136389466 6649
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 21 observations deleted due to missingness## Warning: NAs introduced by coercion## [1] "qua_stress_8"
##
## Call:
## lm(formula = data$qua_stress_1 ~ data$soc_civilstatus + data$VisitAge +
## data$Gender)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.05 -7.08 -5.91 -4.98 990.47
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.28526 4.24881 1.009 0.3132
## data$soc_civilstatus2 1.08700 1.67901 0.647 0.5174
## data$soc_civilstatus3 9.52186 2.17091 4.386 1.16e-05 ***
## data$soc_civilstatus4 0.52700 1.96599 0.268 0.7887
## data$soc_civilstatus5 4.00461 2.73222 1.466 0.1427
## data$soc_civilstatus6 5.73127 3.22279 1.778 0.0754 .
## data$VisitAge 0.06895 0.06858 1.005 0.3147
## data$GenderMale 1.03908 1.17006 0.888 0.3745
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 81.54 on 20514 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.001168, Adjusted R-squared: 0.0008269
## F-statistic: 3.426 on 7 and 20514 DF, p-value: 0.001152
##
## Df Sum Sq Mean Sq F value Pr(>F)
## data$soc_civilstatus 5 146216 29243 4.398 0.000528 ***
## data$VisitAge 1 7994 7994 1.202 0.272856
## data$Gender 1 5243 5243 0.789 0.374519
## Residuals 20514 136389466 6649
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 21 observations deleted due to missingness## Warning: NAs introduced by coercion## [1] "qua_stress_9"
##
## Call:
## lm(formula = data$qua_stress_1 ~ data$soc_civilstatus + data$VisitAge +
## data$Gender)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.05 -7.08 -5.91 -4.98 990.47
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.28526 4.24881 1.009 0.3132
## data$soc_civilstatus2 1.08700 1.67901 0.647 0.5174
## data$soc_civilstatus3 9.52186 2.17091 4.386 1.16e-05 ***
## data$soc_civilstatus4 0.52700 1.96599 0.268 0.7887
## data$soc_civilstatus5 4.00461 2.73222 1.466 0.1427
## data$soc_civilstatus6 5.73127 3.22279 1.778 0.0754 .
## data$VisitAge 0.06895 0.06858 1.005 0.3147
## data$GenderMale 1.03908 1.17006 0.888 0.3745
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 81.54 on 20514 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.001168, Adjusted R-squared: 0.0008269
## F-statistic: 3.426 on 7 and 20514 DF, p-value: 0.001152
##
## Df Sum Sq Mean Sq F value Pr(>F)
## data$soc_civilstatus 5 146216 29243 4.398 0.000528 ***
## data$VisitAge 1 7994 7994 1.202 0.272856
## data$Gender 1 5243 5243 0.789 0.374519
## Residuals 20514 136389466 6649
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 21 observations deleted due to missingness## Warning: NAs introduced by coercion## [1] "qua_stress_10"
##
## Call:
## lm(formula = data$qua_stress_1 ~ data$soc_civilstatus + data$VisitAge +
## data$Gender)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.05 -7.08 -5.91 -4.98 990.47
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.28526 4.24881 1.009 0.3132
## data$soc_civilstatus2 1.08700 1.67901 0.647 0.5174
## data$soc_civilstatus3 9.52186 2.17091 4.386 1.16e-05 ***
## data$soc_civilstatus4 0.52700 1.96599 0.268 0.7887
## data$soc_civilstatus5 4.00461 2.73222 1.466 0.1427
## data$soc_civilstatus6 5.73127 3.22279 1.778 0.0754 .
## data$VisitAge 0.06895 0.06858 1.005 0.3147
## data$GenderMale 1.03908 1.17006 0.888 0.3745
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 81.54 on 20514 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.001168, Adjusted R-squared: 0.0008269
## F-statistic: 3.426 on 7 and 20514 DF, p-value: 0.001152
##
## Df Sum Sq Mean Sq F value Pr(>F)
## data$soc_civilstatus 5 146216 29243 4.398 0.000528 ***
## data$VisitAge 1 7994 7994 1.202 0.272856
## data$Gender 1 5243 5243 0.789 0.374519
## Residuals 20514 136389466 6649
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 21 observations deleted due to missingness