Using logistic regression

HR Analytics: Exploring Employee Data in R

Ben Teusch

HR Analytics Consultant

Using glm() for logistic regression

glm(high_performer ~ salary, data = hr, family = "binomial") %>% 
  tidy()

# A tibble: 2 × 5
  term          estimate  std.error statistic  p.value
  <chr>            <dbl>      <dbl>     <dbl>    <dbl>
1 (Intercept) -4.23      0.275          -15.4 1.56e-53
2 salary       0.0000423 0.00000341      12.4 2.79e-35

glm() vs lm()
family = "binomial"

Multiple logistic regression

glm(high_performer ~ salary + department, 
    data = hr, family = "binomial") %>% 
    tidy()

# A tibble: 4 × 5
  term                estimate  std.error statistic  p.value
  <chr>                  <dbl>      <dbl>     <dbl>    <dbl>
1 (Intercept)       -4.22      0.277        -15.2   1.70e-52
2 salary             0.0000423 0.00000342    12.4   2.91e-35
3 departmentFinance -0.0611    0.309         -0.197 8.44e- 1
4 departmentSales   -0.0461    0.138         -0.334 7.38e- 1

Let's practice!

HR Analytics: Exploring Employee Data in R