Question 1

## # A tibble: 1 x 1
##   mean_education
##            <dbl>
## 1           3.62
## # A tibble: 2 x 2
##   race  mean_education
##   <chr>          <dbl>
## 1 b               3.62
## 2 w               3.62
## # A tibble: 1 x 1
##   mean_jobs
##       <dbl>
## 1      3.66
## # A tibble: 2 x 2
##   race  mean_jobs
##   <chr>     <dbl>
## 1 b          3.66
## 2 w          3.66

Based on the results above we can see that education and jobs are balanced across race. This is important because we are conducting an experiment to test the difference of the callback rate if an applicant had a more White name or an African-American name. Having the variables education and jobs be balanced across the races indiactes that the results will not be influenced by any of these factors and we are able to control for these factors. There is less bias in regards to education and jobs.

Question 2

## [1] 0.08049281

Question 3

## # A tibble: 1 x 1
##   mean_call
##       <dbl>
## 1    0.0805
## # A tibble: 2 x 2
##   race  mean_call
##   <chr>     <dbl>
## 1 b        0.0645
## 2 w        0.0965

The results above show that White sounding names do recieve more callbacks compared to African-American names which indicates that there possibly exists a racial injustice and discrimination within the job application process.

Question 4

## # A tibble: 4 x 3
## # Groups:   race [2]
##   race  gender mean_call
##   <chr> <chr>      <dbl>
## 1 b     f         0.0663
## 2 b     m         0.0583
## 3 w     f         0.0989
## 4 w     m         0.0887

The results suggest that between male and female, females recieve more callbacks within each race. However, when comparing females across the races it is evident that White females recieve more callbacks than African-American females which further proves discrimination. It is also evident that White males are called back more than African-American males.