Premium Project

Are You Ready for the Zombie Apocalypse?

Use your logistic regression skills to protect people from becoming zombies!

Start Project
  • 10 tasks
  • 730 participants
  • 1,500 XP

Project Description

It is no longer just a threat; it is now a reality! Zombies have been spotted all over the U.S. Work with your colleagues at the Centers for Disease Control and Prevention to identify the characteristics and supplies that seem to keep humans safe. Develop a logistic regression model that predicts the probability of becoming a zombie based on personal characteristics like age and sex, type of neighborhood people live in, and what emergency supplies are available. Practice bivariate tests like chi-squared and t-test to identify the most relevant variables for the model, develop and run the model, check model assumptions, and use the model to predict the probability of becoming a zombie for friends, family, and even yourself!

To complete this Project, you should be comfortable with basic data analysis in base R and visualization in ggplot2, and be familiar with chi-squared test, t-tests, and logistic regression. We recommend that you take Multiple and Logistic Regression before starting this Project.

The zombies dataset was created for this Project based on emergency preparedness recommendations. It consists of 200 observations and 14 variables. The variables include personal characteristics like age and sex, zombie status, a description of the neighborhood each participant lives in, and measures of supplies.

Project Tasks

  • 2Compare zombies and humans
  • 3Compare zombies and humans (part 2)
  • 4Recode variables missing values
  • 5Selecting variables to predict zombie status
  • 6Build the model
  • 7Checking model assumptions
  • 8Interpreting assumptions and making predictions
  • 9What is your zombie probability?
  • 10Are you ready for the zombie apocalypse?
Jenine Harris

Associate Professor at Washington University in St. Louis

Jenine teaches biostatistics in the public health program at Washington University in St. Louis and is the author of the Sage book on statistical models for social networks, "An Introduction to Exponential Random Graph Modeling." She is an enthusiastic R user who co-leads the R-Ladies chapter in St. Louis and has an interest in reproducible research practices.

See More


  • R LogoR
  • Topics

    Data ManipulationData VisualizationProbability & StatisticsImporting & Cleaning Data