## 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
## -10.7196 -2.7334 -0.5827 2.1024 27.9579
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 24.605850 0.208257 118.151 < 2e-16 ***
## soc_civilstatus2 0.075824 0.082184 0.923 0.35622
## soc_civilstatus3 0.426554 0.106648 4.000 6.37e-05 ***
## soc_civilstatus4 0.051345 0.096639 0.531 0.59521
## soc_civilstatus5 -0.379683 0.134422 -2.825 0.00474 **
## soc_civilstatus6 0.132889 0.159211 0.835 0.40391
## soc_civilstatus998 0.711440 0.884854 0.804 0.42140
## VisitAge 0.019189 0.003368 5.697 1.24e-08 ***
## GenderMale 1.063900 0.057388 18.539 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.954 on 20051 degrees of freedom
## (483 observations deleted due to missingness)
## Multiple R-squared: 0.0209, Adjusted R-squared: 0.02051
## F-statistic: 53.5 on 8 and 20051 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = BMI ~ soc_civilstatus + VisitAge + Gender, data = data)
##
## Standardized Coefficients::
## (Intercept) soc_civilstatus2 soc_civilstatus3
## 0.000000000 0.006690117 0.028637407
## soc_civilstatus4 soc_civilstatus5 soc_civilstatus6
## 0.003839591 -0.020045816 0.005994277
## soc_civilstatus998 VisitAge GenderMale
## 0.005620526 0.040839213 0.131963605## Df Sum Sq Mean Sq F value Pr(>F)
## soc_civilstatus 6 433 72 4.618 0.000107 ***
## VisitAge 1 885 885 56.618 5.51e-14 ***
## Gender 1 5372 5372 343.680 < 2e-16 ***
## Residuals 20051 313434 16
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 483 observations deleted due to missingnessAnalysis 2: Compare sle_needhrs_rested among different social civil status after controlling for age and gender
##
## Call:
## lm(formula = sle_needhrs_rested ~ soc_civilstatus + VisitAge +
## Gender, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -102.48 -34.66 -31.62 -28.62 973.60
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 31.4340 9.2338 3.404 0.000665 ***
## soc_civilstatus2 -5.7743 3.6501 -1.582 0.113680
## soc_civilstatus3 8.8745 4.7203 1.880 0.060112 .
## soc_civilstatus4 6.8314 4.2747 1.598 0.110035
## soc_civilstatus5 -8.2142 5.9408 -1.383 0.166781
## soc_civilstatus6 18.9964 7.0074 2.711 0.006715 **
## soc_civilstatus998 67.6748 39.6784 1.706 0.088101 .
## VisitAge 0.1074 0.1490 0.721 0.471128
## GenderMale -3.8648 2.5428 -1.520 0.128557
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 177.3 on 20534 degrees of freedom
## Multiple R-squared: 0.001356, Adjusted R-squared: 0.0009666
## F-statistic: 3.484 on 8 and 20534 DF, p-value: 0.0005017##
## Call:
## lm(formula = sle_needhrs_rested ~ soc_civilstatus + VisitAge +
## Gender, data = data)
##
## Standardized Coefficients::
## (Intercept) soc_civilstatus2 soc_civilstatus3
## 0.000000000 -0.011446706 0.013435058
## soc_civilstatus4 soc_civilstatus5 soc_civilstatus6
## 0.011528386 -0.009793518 0.019429722
## soc_civilstatus998 VisitAge GenderMale
## 0.011898824 0.005154499 -0.010797561## Df Sum Sq Mean Sq F value Pr(>F)
## soc_civilstatus 6 793131 132188 4.205 0.000311 ***
## VisitAge 1 10440 10440 0.332 0.564411
## Gender 1 72612 72612 2.310 0.128557
## Residuals 20534 645451615 31433
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Analysis 3: Compare sle_sleephrs among different social civil status after controlling for age and gender
##
## Call:
## lm(formula = sle_sleephrs ~ soc_civilstatus + VisitAge + Gender,
## data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -51.87 -3.90 -2.39 -0.84 993.62
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.833862 2.549985 2.680 0.007369 **
## soc_civilstatus2 0.444957 1.008022 0.441 0.658916
## soc_civilstatus3 1.518921 1.303557 1.165 0.243947
## soc_civilstatus4 1.412550 1.180499 1.197 0.231488
## soc_civilstatus5 2.011547 1.640607 1.226 0.220175
## soc_civilstatus6 0.284662 1.935146 0.147 0.883054
## soc_civilstatus998 47.941019 10.957562 4.375 1.22e-05 ***
## VisitAge 0.001484 0.041159 0.036 0.971234
## GenderMale -2.531129 0.702220 -3.604 0.000314 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 48.96 on 20534 degrees of freedom
## Multiple R-squared: 0.001827, Adjusted R-squared: 0.001438
## F-statistic: 4.698 on 8 and 20534 DF, p-value: 9.108e-06##
## Call:
## lm(formula = sle_sleephrs ~ soc_civilstatus + VisitAge + Gender,
## data = data)
##
## Standardized Coefficients::
## (Intercept) soc_civilstatus2 soc_civilstatus3
## 0.0000000000 0.0031932930 0.0083246721
## soc_civilstatus4 soc_civilstatus5 soc_civilstatus6
## 0.0086297523 0.0086824809 0.0010540523
## soc_civilstatus998 VisitAge GenderMale
## 0.0305156418 0.0002578667 -0.0256009467## Df Sum Sq Mean Sq F value Pr(>F)
## soc_civilstatus 6 58707 9785 4.082 0.000426 ***
## VisitAge 1 237 237 0.099 0.753412
## Gender 1 31145 31145 12.992 0.000314 ***
## Residuals 20534 49224620 2397
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Analysis 4: Compare sle_general_gen among different social civil status after controlling for age and gender
##
## 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 67.038
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.6439265 0.0589797 61.783 < 2e-16 ***
## soc_civilstatus2 0.0032204 0.0233150 0.138 0.8901
## soc_civilstatus3 -0.1429843 0.0301505 -4.742 2.13e-06 ***
## soc_civilstatus4 -0.1247302 0.0273043 -4.568 4.95e-06 ***
## soc_civilstatus5 -0.0386081 0.0379463 -1.017 0.3090
## soc_civilstatus6 -0.0764020 0.0447588 -1.707 0.0878 .
## soc_civilstatus998 -0.0007891 0.2534421 -0.003 0.9975
## VisitAge 0.0009765 0.0009520 1.026 0.3050
## GenderMale 0.2481291 0.0162419 15.277 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.132 on 20534 degrees of freedom
## Multiple R-squared: 0.0152, Adjusted R-squared: 0.01481
## F-statistic: 39.61 on 8 and 20534 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
## 0.000000e+00 9.925156e-04 -3.365327e-02
## soc_civilstatus4 soc_civilstatus5 soc_civilstatus6
## -3.272450e-02 -7.156470e-03 -1.214911e-02
## soc_civilstatus998 VisitAge GenderMale
## -2.156904e-05 7.285265e-03 1.077771e-01## Df Sum Sq Mean Sq F value Pr(>F)
## soc_civilstatus 6 99 16.49 12.855 1.49e-14 ***
## VisitAge 1 8 8.12 6.328 0.0119 *
## Gender 1 299 299.31 233.388 < 2e-16 ***
## Residuals 20534 26334 1.28
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Analysis 5: Compare die_meals among different social civil status after controlling for age and gender
##
## Call:
## lm(formula = alc_drink_frq ~ soc_civilstatus + VisitAge + Gender,
## data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.48 -2.61 -1.65 -0.43 994.71
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.94999 2.05352 0.463 0.6436
## soc_civilstatus2 0.14098 0.80763 0.175 0.8614
## soc_civilstatus3 1.89600 1.05029 1.805 0.0711 .
## soc_civilstatus4 0.41406 0.95028 0.436 0.6630
## soc_civilstatus5 -0.30386 1.31314 -0.231 0.8170
## soc_civilstatus6 1.37286 1.56557 0.877 0.3805
## soc_civilstatus998 -0.74329 9.46731 -0.079 0.9374
## VisitAge 0.06101 0.03314 1.841 0.0656 .
## GenderMale -0.58715 0.56451 -1.040 0.2983
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 39 on 20153 degrees of freedom
## (381 observations deleted due to missingness)
## Multiple R-squared: 0.0004475, Adjusted R-squared: 5.071e-05
## F-statistic: 1.128 on 8 and 20153 DF, p-value: 0.3405##
## Call:
## lm(formula = alc_drink_frq ~ soc_civilstatus + VisitAge + Gender,
## data = data)
##
## Standardized Coefficients::
## (Intercept) soc_civilstatus2 soc_civilstatus3
## 0.0000000000 0.0012761951 0.0130294984
## soc_civilstatus4 soc_civilstatus5 soc_civilstatus6
## 0.0031746870 -0.0016556415 0.0063449562
## soc_civilstatus998 VisitAge GenderMale
## -0.0005531147 0.0132971052 -0.0074633853## Df Sum Sq Mean Sq F value Pr(>F)
## soc_civilstatus 6 7426 1238 0.814 0.5592
## VisitAge 1 4655 4655 3.059 0.0803 .
## Gender 1 1646 1646 1.082 0.2983
## Residuals 20153 30660072 1521
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 381 observations deleted due to missingnessAnalysis 6: Compare alc_drink_summ among different social civil status after controlling for age and gender
##
## Call:
## lm(formula = alc_drink_summ ~ soc_civilstatus + VisitAge + Gender,
## data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -128.23 -43.00 -36.51 -28.53 982.02
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -10.2199 10.2230 -1.000 0.31747
## soc_civilstatus2 -8.3962 4.0206 -2.088 0.03678 *
## soc_civilstatus3 49.0825 5.2287 9.387 < 2e-16 ***
## soc_civilstatus4 12.7032 4.7308 2.685 0.00725 **
## soc_civilstatus5 -0.9408 6.5372 -0.144 0.88557
## soc_civilstatus6 13.4883 7.7939 1.731 0.08354 .
## soc_civilstatus998 83.3233 47.1313 1.768 0.07709 .
## VisitAge 0.7689 0.1650 4.661 3.17e-06 ***
## GenderMale 0.2307 2.8103 0.082 0.93456
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 194.2 on 20153 degrees of freedom
## (381 observations deleted due to missingness)
## Multiple R-squared: 0.006612, Adjusted R-squared: 0.006218
## F-statistic: 16.77 on 8 and 20153 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
## 0.0000000000 -0.0152197143 0.0675444369
## soc_civilstatus4 soc_civilstatus5 soc_civilstatus6
## 0.0195041173 -0.0010264919 0.0124834122
## soc_civilstatus998 VisitAge GenderMale
## 0.0124164667 0.0335570818 0.0005873327## Df Sum Sq Mean Sq F value Pr(>F)
## soc_civilstatus 6 4227862 704644 18.688 < 2e-16 ***
## VisitAge 1 829492 829492 22.000 2.75e-06 ***
## Gender 1 254 254 0.007 0.935
## Residuals 20153 759867772 37705
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 381 observations deleted due to missingnessAnalysis 7: Compare scr_sc23 among different social civil status after controlling for age and gender
##
## Call:
## lm(formula = scr_sc23 ~ soc_civilstatus + VisitAge + Gender,
## data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -250.79 -14.26 -12.87 -11.85 986.84
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.24427 6.17092 1.336 0.18157
## soc_civilstatus2 -1.01353 2.43940 -0.415 0.67779
## soc_civilstatus3 10.45536 3.15459 3.314 0.00092 ***
## soc_civilstatus4 6.47941 2.85679 2.268 0.02333 *
## soc_civilstatus5 0.93197 3.97024 0.235 0.81441
## soc_civilstatus6 5.43828 4.68302 1.161 0.24554
## soc_civilstatus998 236.59810 26.51711 8.922 < 2e-16 ***
## VisitAge 0.08922 0.09961 0.896 0.37038
## GenderMale 1.23356 1.69936 0.726 0.46791
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 118.5 on 20534 degrees of freedom
## Multiple R-squared: 0.004667, Adjusted R-squared: 0.004279
## F-statistic: 12.03 on 8 and 20534 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = scr_sc23 ~ soc_civilstatus + VisitAge + Gender,
## data = data)
##
## Standardized Coefficients::
## (Intercept) soc_civilstatus2 soc_civilstatus3
## 0.000000000 -0.003001427 0.023645032
## soc_civilstatus4 soc_civilstatus5 soc_civilstatus6
## 0.016334239 0.001659910 0.008309276
## soc_civilstatus998 VisitAge GenderMale
## 0.062143481 0.006396402 0.005148376## Df Sum Sq Mean Sq F value Pr(>F)
## soc_civilstatus 6 1330983 221830 15.801 <2e-16 ***
## VisitAge 1 13224 13224 0.942 0.332
## Gender 1 7397 7397 0.527 0.468
## Residuals 20534 288274903 14039
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1