Exercise 1 - Confidence Intervals of Polling Data

For each poll in the polling data set, use the CLT to create a 95% confidence interval for the spread. Create a new table called cis that contains columns for the lower and upper limits of the confidence intervals.

This exercise is part of the course

HarvardX Data Science Module 4 - Inference and Modeling

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Exercise instructions

Use pipes %>% to pass the poll object on to the mutate function, which creates new variables.
Create a variable called X_hat that contains the estimate of the proportion of Clinton voters for each poll.
Create a variable called se that contains the standard error of the spread.
Calculate the confidence intervals using the qnorm function and your calculated se.
Use the select function to keep the following columns: state, startdate, enddate, pollster, grade, spread, lower, upper.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Load the libraries and data
library(dplyr)
library(dslabs)
data("polls_us_election_2016")

# Create a table called `polls` that filters by  state, date, and reports the spread
polls <- polls_us_election_2016 %>% 
  filter(state != "U.S." & enddate >= "2016-10-31") %>% 
  mutate(spread = rawpoll_clinton/100 - rawpoll_trump/100)

# Create an object called `cis` that has the columns indicated in the instructions

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