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

Looking at the results, there is no difference between education and jobs. This is important because having variables that are the same is helpful as there would be less bias on the outcome. Since we are focusing callback rates that are based on names (i.e. African American or white American name), controlling for the education and job variables helps us produce a less biased outcome (callback rates).

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 suggest that white American-sounding names are more privileged than African American-sounding names. This means that resumes with white American names receive more calls than resumes with African American sounding names. This indicates that discrimination exists within the employment 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

Looking at the results, we can see that females receive more callbacks than males in each race. Even though females receive more callbacks than males in each race, white American female names are still more privileged (white American female names receive more callbacks than African American female names) than African American female names. The same applies to white American male name s, as they receive more callbacks than African American male names.