“Robert Martin was laid off from his job at Westvaco, Inc. soon after he turned 54 years old. He sued for age discrimination.”
What kind of data would we need to evaluate this claim?
| ages of employees in one department ( |
|---|
| 25, 33, 35, 38, 48, |
What makes a data structure tidy?
- structure 1 is not tidy because each row is not an observation
- name it Westvaco
- start with two vectors, use
data.frame( ) - print out the data frame
age <- c(25,33,35,38,48,55,55,55,56,64)
fired <- c("no", "no", "no", "no", "no", "no", "yes", "yes", "no", "yes")
westvaco <- data.frame(age,fired)
westvaco
- make a histogram of age
- make a faceted histogram of age by fired
- make a boxplot of age by fired
gf_histogram(~ age, data=westvaco, fill="blue", binwidth=1)%>%
gf_facet_grid(fired ~.)
Based on these data, do you think Westvaco's pattern of firings shows evidence of age discrimination?
- fired = age + other stuff
- what are some other possible models
- data = model + error
Based on the data, do you think the pattern of firings could be explained by a purely random DGP?
Key point: you can't see evidence of randomness by just looking at a single outcome. (You have to flip a coin more than once to tell if it is fair.)
Adding in Sampling Distributions