Q1. Is random sampling employed in the study described below?

yes

Q2. According to your answer in Q1, would it be appropriate to apply the findings back to the population as a whole?

It would not be appropriate to apply the findings back to the population as a whole.

Q3. How many people applied for UC Berkeley?

4,526

Q4. How many women applied for UC Berkeley?

1,835

# Load packages
library(dplyr)

ucb_admit <- read.csv("/resources/rstudio/BusinessStatistics/data/ucb_admit.csv") 
ucb_admit$Dept <- as.factor(ucb_admit$Dept)
str(ucb_admit)
## 'data.frame':    4526 obs. of  3 variables:
##  $ Admit : Factor w/ 2 levels "Admitted","Rejected": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Gender: Factor w/ 2 levels "Female","Male": 2 2 2 2 2 2 2 2 2 2 ...
##  $ Dept  : Factor w/ 6 levels "A","B","C","D",..: 1 1 1 1 1 1 1 1 1 1 ...
summary(ucb_admit)
##       Admit         Gender     Dept   
##  Admitted:1755   Female:1835   A:933  
##  Rejected:2771   Male  :2691   B:585  
##                                C:918  
##                                D:792  
##                                E:584  
##                                F:714
head(ucb_admit)
##      Admit Gender Dept
## 1 Admitted   Male    A
## 2 Admitted   Male    A
## 3 Admitted   Male    A
## 4 Admitted   Male    A
## 5 Admitted   Male    A
## 6 Admitted   Male    A

Q5. How many women applied for Department A?

89

# Count number of male and female applicants admitted
ucb_admit %>%
  count(Dept, Admit, Gender)
## # A tibble: 24 x 4
##    Dept  Admit    Gender     n
##    <fct> <fct>    <fct>  <int>
##  1 A     Admitted Female    89
##  2 A     Admitted Male     512
##  3 A     Rejected Female    19
##  4 A     Rejected Male     313
##  5 B     Admitted Female    17
##  6 B     Admitted Male     353
##  7 B     Rejected Female     8
##  8 B     Rejected Male     207
##  9 C     Admitted Female   202
## 10 C     Admitted Male     120
## # ... with 14 more rows

Q6. Of all the women applied for UC Berkeley, what percentage were rejected?

69.6%

ucb_admit %>%
  count(Gender, Admit) %>%
  # Group by gender
  group_by(Gender) %>%
  # Create new variable
  mutate(prop = n / sum(n)) %>%
  # Filter for admitted
  filter(Gender == "Female")
## # A tibble: 2 x 4
## # Groups:   Gender [1]
##   Gender Admit        n  prop
##   <fct>  <fct>    <int> <dbl>
## 1 Female Admitted   557 0.304
## 2 Female Rejected  1278 0.696

Q7. What type of sampling method is this?

Stratified Sampling