Exercise 1 - Comparing Proportions of Hits

In a previous exercise, we determined whether or not each poll predicted the correct winner for their state in the 2016 U.S. presidential election. Each poll was also assigned a grade by the poll aggregator. Now we're going to determine if polls rated A- made better predictions than polls rated C-.

In this exercise, filter the errors data for just polls with grades A- and C-. Calculate the proportion of times each grade of poll predicted the correct winner.

This exercise is part of the course

HarvardX Data Science Module 4 - Inference and Modeling

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Exercise instructions

Filter errors for grades A- and C-.
Group the data by grade and hit.
Summarize the number of hits for each grade.
Generate a two-by-two table containing the number of hits and misses for each grade. Try using the spread function to generate this table.
Calculate the proportion of times each grade was correct.

Hands-on interactive exercise

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

# The 'errors' data have already been loaded. Examine them using the `head` function.
head(errors)

# Generate an object called 'totals' that contains the numbers of good and bad predictions for polls rated A- and C-






# Print the proportion of hits for grade A- polls to the console


# Print the proportion of hits for grade C- polls to the console

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