Linear regression on the United States

A linear regression is a model that lets us examine how one variable changes with respect to another by fitting a best fit line. It is done with the lm() function in R.

Here, you'll fit a linear regression to just the percentage of "yes" votes from the United States.

This exercise is part of the course

Case Study: Exploratory Data Analysis in R

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Exercise instructions

Print the US_by_year data to the console.
Using just the US data in US_by_year, use lm() to run a linear regression predicting percent_yes from year. Save this to a variable US_fit.
Summarize US_fit using the summary() function.

Hands-on interactive exercise

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

# Percentage of yes votes from the US by year: US_by_year
US_by_year <- by_year_country %>%
  filter(country == "United States")

# Print the US_by_year data


# Perform a linear regression of percent_yes by year: US_fit


# Perform summary() on the US_fit object

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