Bootstrapping and Confidence Intervals

Based on Chater 8 of ModernDive. Code for Quiz 12.

Load the R package we will use.

library(tidyverse)
library(moderndive)
library(infer)
library(fivethirtyeight)

• Replace all the instances of ???. These are answers on your moodle quiz.
• Run all the individual code chunks to make sure the answers in this file correspond with your quiz answers
• After you check all your code chunks run then you can knit it. It won’t knit until the ??? are replaced.
• Save a plot to be your preview plot
• Look at the variable definitions in congress_age

What is the average age of members that have served in congress? - Set random seed generator to 123 - Take a sample of 100 from the dataset congress_age and assign it to congress_age_100

set.seed(123)
congress_age_100 <- congress_age %>%
  rep_sample_n(size = 100)

• congress_age is the population and congress_age_100 is the sample
• 18635 is number of observations in the population and 100 is the number of observations in your sample

Construct the confidence interval

1. Use specify to indicate the variable from congress_age_100 that you are interested in

congress_age_100 %>% 
  specify(response = age)

Response: age (numeric)
# A tibble: 100 x 1
     age
   <dbl>
 1  53.1
 2  54.9
 3  65.3
 4  60.1
 5  43.8
 6  57.9
 7  55.3
 8  46  
 9  42.1
10  37  
# ... with 90 more rows

2.Generate 1000 replicates of your sample of 100

The output has 100,000 rows

3. Calculate the mean for each replicate

• Assign the bootstrap_distribution_mean_age
• Display bootstrap_distribution_mean_age

  bootstrap_distribution_mean_age <- congress_age_100 %>% specify(response = age) %>% generate(reps = 1000, type = “bootstrap”) %>% calculate(stat = “mean”)

bootstrap_distribution_mean_age