Infer whether gender differences in mean life satisfaction are evident among working professionals.

require(dplyr)
## Loading required package: dplyr
## 
## Attaching package: 'dplyr'
## 
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## 
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
require(magrittr)
## Loading required package: magrittr
pwces <-read.csv(url("http://www.personal.psu.edu/dlp/WFED540/pwces.csv"))
summary(pwces)
##      gender          lifesat    
##  Min.   :0.0000   Min.   : 5.0  
##  1st Qu.:0.0000   1st Qu.:18.0  
##  Median :0.0000   Median :23.0  
##  Mean   :0.4111   Mean   :21.5  
##  3rd Qu.:1.0000   3rd Qu.:25.0  
##  Max.   :1.0000   Max.   :30.0  
##  NA's   :70       NA's   :78
data<-pwces %>% filter((gender>=0 & gender<=1), (lifesat>=5 & lifesat<=30))
summary(data)
##      gender          lifesat     
##  Min.   :0.0000   Min.   : 5.00  
##  1st Qu.:0.0000   1st Qu.:18.00  
##  Median :0.0000   Median :23.00  
##  Mean   :0.4104   Mean   :21.48  
##  3rd Qu.:1.0000   3rd Qu.:25.00  
##  Max.   :1.0000   Max.   :30.00
t.test(data$lifesat~data$gender)
## 
##  Welch Two Sample t-test
## 
## data:  data$lifesat by data$gender
## t = -1.3392, df = 578.22, p-value = 0.181
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -1.4771301  0.2794267
## sample estimates:
## mean in group 0 mean in group 1 
##        21.23869        21.83755

The result of t-test shows that t-value is -1.3392 and p-value is 0.181. Therefore, the null hypothesis is rejected and the life satisfaction between female and male among working professionals is different.