Age
y = MeasurementData1$Age0
x = MeasurementData1$Race
t.test(y~x)
##
## Welch Two Sample t-test
##
## data: y by x
## t = 0.307, df = 1868, p-value = 0.7589
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.6803 0.9328
## sample estimates:
## mean in group 0 mean in group 1
## 47.90 47.77
Depression
y = MeasurementData1$CES
x = MeasurementData1$Race
t.test(y~x)
##
## Welch Two Sample t-test
##
## data: y by x
## t = -0.8975, df = 1829, p-value = 0.3696
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.4522 0.5404
## sample estimates:
## mean in group 0 mean in group 1
## 14.51 14.96
Education
fit <- aov(Educ ~ Race, data=MeasurementData1)
summary(fit)
## Df Sum Sq Mean Sq F value Pr(>F)
## Race 1 115 115.3 12.9 0.00034
## Residuals 2075 18546 8.9
Employment
fit <- aov(Employment01 ~ Race, data=MeasurementData1)
summary(fit)
## Df Sum Sq Mean Sq F value Pr(>F)
## Race 1 2 1.664 6.84 0.009
## Residuals 2075 504 0.243
Neighborhood
fit <- aov(Neighborhood02 ~ Race, data=MeasurementData1)
summary(fit)
## Df Sum Sq Mean Sq F value Pr(>F)
## Race 1 0 0.097 0.09 0.77
## Residuals 2075 2360 1.137
Income
fit <- aov(acasiIncomx01 ~ Race, data=MeasurementData1)
summary(fit)
## Df Sum Sq Mean Sq F value Pr(>F)
## Race 1 20 20.40 85.1 <2e-16
## Residuals 2075 497 0.24
Self-Rated Health
fit <- aov(sHealthNum ~ Race, data=MeasurementData1)
summary(fit)
## Df Sum Sq Mean Sq F value Pr(>F)
## Race 1 1 0.588 1.02 0.31
## Residuals 2075 1201 0.579
MacArthur Social Status Ladder
#ANOVA
fit <- aov(MCLcountry ~ Race, data=MeasurementData1)
summary(fit)
## Df Sum Sq Mean Sq F value Pr(>F)
## Race 1 26 25.57 6.42 0.011
## Residuals 2075 8266 3.98
#t-test
y = MeasurementData1$MCLcountry
x = MeasurementData1$Race
t.test(y~x)
##
## Welch Two Sample t-test
##
## data: y by x
## t = 2.559, df = 1953, p-value = 0.01058
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.05245 0.39679
## sample estimates:
## mean in group 0 mean in group 1
## 4.460 4.236
Age
y = MeasurementData1$Age0
x = MeasurementData1$Sex
t.test(y~x)
##
## Welch Two Sample t-test
##
## data: y by x
## t = -0.5605, df = 1940, p-value = 0.5752
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.0289 0.5715
## sample estimates:
## mean in group 0 mean in group 1
## 47.75 47.98
Depression
y = MeasurementData1$CES
x = MeasurementData1$Sex
t.test(y~x)
##
## Welch Two Sample t-test
##
## data: y by x
## t = 3.307, df = 2034, p-value = 0.0009598
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.6619 2.5909
## sample estimates:
## mean in group 0 mean in group 1
## 15.40 13.78
Education
fit <- aov(Educ ~ Sex, data=MeasurementData1)
summary(fit)
## Df Sum Sq Mean Sq F value Pr(>F)
## Sex 1 1 0.89 0.1 0.75
## Residuals 2075 18660 8.99
Employment
fit <- aov(Employment01 ~ Sex, data=MeasurementData1)
summary(fit)
## Df Sum Sq Mean Sq F value Pr(>F)
## Sex 1 3 3.43 14.1 0.00017
## Residuals 2075 503 0.24
Neighborhood
fit <- aov(Neighborhood02 ~ Sex, data=MeasurementData1)
summary(fit)
## Df Sum Sq Mean Sq F value Pr(>F)
## Sex 1 5 4.74 4.18 0.041
## Residuals 2075 2355 1.14
Income
fit <- aov(acasiIncomx01 ~ Sex, data=MeasurementData1)
summary(fit)
## Df Sum Sq Mean Sq F value Pr(>F)
## Sex 1 3 2.725 11 0.00094
## Residuals 2075 515 0.248
Self-Rated Health
fit <- aov(sHealthNum ~ Sex, data=MeasurementData1)
summary(fit)
## Df Sum Sq Mean Sq F value Pr(>F)
## Sex 1 2 1.984 3.43 0.064
## Residuals 2075 1199 0.578
MacArthur Social Status Ladder
#ANOVA
fit <- aov(MCLcountry ~ Sex, data=MeasurementData1)
summary(fit)
## Df Sum Sq Mean Sq F value Pr(>F)
## Sex 1 8 7.87 1.97 0.16
## Residuals 2075 8284 3.99
#t-test
y = MeasurementData1$MCLcountry
x = MeasurementData1$Sex
t.test(y~x)
##
## Welch Two Sample t-test
##
## data: y by x
## t = -1.403, df = 1919, p-value = 0.1607
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.29806 0.04942
## sample estimates:
## mean in group 0 mean in group 1
## 4.312 4.436