Forecasting US Presidential Elections with Mixed Models

Offered By
Coursera Project Network
In this Guided Project, you will:

Learn how the US elects Presidents in the Electoral College

Understand the basics of mixed effects models

Build a forecasting model to simulate the election using mixed effects models

Clock2 hours
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

In this project-based course, you will learn how to forecast US Presidential Elections. We will use mixed effects models in the R programming language to build a forecasting model for the 2020 election. The project will review how the US selects Presidents in the Electoral College, stylized facts about voting trends, the basics of mixed effects models, and how to use them in forecasting.

Skills you will develop

ForecastingElectionLinear RegressionStatistical ModelsMixed Model

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Overview of Forecasting Elections (Lecture)

  2. Overview of How the US Elects Presidents (Lecture)

  3. Stylized Facts About Voting (Lecture)

  4. Types of Forecasting Models (Lecture)

  5. Building a Fundamentals Based Forecasting Model (Lecture)

  6. Setting Up the Dataset (Coding)

  7. Fitting the Model (Coding)

  8. Extracting Variances (Coding)

  9. Simulating Errors (Coding)

  10. Viewing the Winner (Coding)

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

Frequently asked questions

Frequently Asked Questions

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