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Vinod Bakthavachalam
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/agy3ln/forecasting
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Syllabus
Forecasting US Presidential Elections with Mixed Models
Welcome to this project-based course on forecasting US Presidential elections. In this project, you will learn the basics of how the US elects Presidents, mixed effects models, and how to apply them to forecast the 2020 Presidential election!
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides knowledge in forecasting US Presidential Elections
Instructors are Vinod Bakthavachalam
Reviews electoral college selection of Presidents
Uses mixed effects models for prediction
Involves the R programming language
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Mixed models election forecast
According to students, this course on forecasting US presidential elections with mixed models is easy to understand and engaging. Despite this, the course materials are only available for a short duration after initially taking the course. If you want to keep your materials, you can download the videos and upload them to YouTube for future access.
Download the videos for offline access.
"It is very easy to download the videos."
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Easy to understand and engaging.
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"You can experience, how it could be done."
Course materials are not always accessible.
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Forecasting US Presidential Elections with Mixed Models with these
activities:
Review basic statistical concepts
Show steps
Refreshing your basic statistical knowledge will strengthen your foundation for the more advanced concepts covered in the course.
Browse courses on
Statistics
Show steps
-
Review notes or textbooks on statistical concepts
-
Solve practice problems to test your understanding
Follow online tutorials on election forecasting
Show steps
External tutorials can provide additional insights and perspectives on election forecasting, complementing the course material.
Show steps
-
Identify relevant and reputable tutorials
-
Follow the tutorials and take notes
-
Apply the concepts learned to the course context
Write a summary of the Electoral College system
Show steps
Summarizing the Electoral College system will enhance your understanding of its fonctionnement and how it impacts election results.
Browse courses on
Electoral College
Show steps
-
Research and gather information about the Electoral College
-
Outline the main points and structure of your summary
-
Write a clear and concise summary
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Review US Presidential Elections
Show steps
Review key concepts and facts about US Presidential elections to build a stronger foundation for understanding the course content.
Show steps
-
Read through the articles and resources provided in the course materials on US Presidential elections.
-
Create a timeline of key events in US Presidential election history.
-
Research and summarize the different methods used to select Presidents in the Electoral College.
Review additional course texts
Show steps
Agresti's book delves into statistical methods used throughout the course. Reviewing the chapters used in this course can help you master these methods.
Browse courses on
Statistical Methods
Show steps
-
Identify chapters relevant to course topics
-
Review key concepts and formulas
-
Solve practice exercises to check your understanding (Optional)
Solve practice problems on mixed effects models
Show steps
Solving practice problems will reinforce your understanding of mixed effects models and improve your ability to apply them in forecasting.
Browse courses on
Mixed Effects Models
Show steps
-
Identify and gather practice problems
-
Attempt to solve problems independently
-
Review and compare solutions
Solve Practice Problems on Mixed Effects Models
Show steps
Reinforce your knowledge of mixed effects models by solving practice problems.
Browse courses on
Mixed Effects Models
Show steps
-
Find a collection of practice problems on mixed effects models.
-
Solve the problems without looking at the solutions.
-
Check your answers against the solutions.
Engage in discussion forums with classmates
Show steps
Engaging with other learners in discussions can foster a deeper understanding of course concepts and different perspectives.
Show steps
-
Participate in ongoing discussions
-
Ask clarifying questions
-
Share insights and experiences
Practice Building a Mixed Effects Model in R
Show steps
Solidify your understanding of mixed effects models by building one yourself in R.
Browse courses on
Mixed Effects Models
Show steps
-
Review the concepts of mixed effects models.
-
Choose a dataset to analyze.
-
Build the mixed effects model in R.
-
Interpret the results of the model.
Create a visual representation of the forecasting model
Show steps
Visualizing your forecasting model will help you gain insights into its structure and assumptions, and identify potential areas for improvement.
Browse courses on
Data Visualization
Show steps
-
Choose appropriate visualization techniques
-
Create visualizations using data analysis tools
-
Interpret and analyze the visualizations
Build a mock election forecasting model
Show steps
Creating a mock forecasting model will allow you to practice applying the concepts learned in the course and gain hands-on experience in election forecasting.
Browse courses on
Mixed Effects Models
Show steps
-
Gather historical election data
-
Choose appropriate mixed effects model
-
Estimate model parameters
-
Validate model performance
-
Interpret and present results
Review basic statistical concepts
Show steps
Refreshing your basic statistical knowledge will strengthen your foundation for the more advanced concepts covered in the course.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Review notes or textbooks on statistical concepts
- Solve practice problems to test your understanding
Follow online tutorials on election forecasting
Show steps
External tutorials can provide additional insights and perspectives on election forecasting, complementing the course material.
Show steps
Show steps
Show steps
- Identify relevant and reputable tutorials
- Follow the tutorials and take notes
- Apply the concepts learned to the course context
Write a summary of the Electoral College system
Show steps
Summarizing the Electoral College system will enhance your understanding of its fonctionnement and how it impacts election results.
Browse courses on
Electoral College
Show steps
Show steps
Show steps
- Research and gather information about the Electoral College
- Outline the main points and structure of your summary
- Write a clear and concise summary
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Review US Presidential Elections
Show steps
Review key concepts and facts about US Presidential elections to build a stronger foundation for understanding the course content.
Show steps
Show steps
Show steps
- Read through the articles and resources provided in the course materials on US Presidential elections.
- Create a timeline of key events in US Presidential election history.
- Research and summarize the different methods used to select Presidents in the Electoral College.
Review additional course texts
Show steps
Agresti's book delves into statistical methods used throughout the course. Reviewing the chapters used in this course can help you master these methods.
Browse courses on
Statistical Methods
Show steps
Show steps
Show steps
- Identify chapters relevant to course topics
- Review key concepts and formulas
- Solve practice exercises to check your understanding (Optional)
Solve practice problems on mixed effects models
Show steps
Solving practice problems will reinforce your understanding of mixed effects models and improve your ability to apply them in forecasting.
Browse courses on
Mixed Effects Models
Show steps
Show steps
Show steps
- Identify and gather practice problems
- Attempt to solve problems independently
- Review and compare solutions
Solve Practice Problems on Mixed Effects Models
Show steps
Reinforce your knowledge of mixed effects models by solving practice problems.
Browse courses on
Mixed Effects Models
Show steps
Show steps
Show steps
- Find a collection of practice problems on mixed effects models.
- Solve the problems without looking at the solutions.
- Check your answers against the solutions.
Engage in discussion forums with classmates
Show steps
Engaging with other learners in discussions can foster a deeper understanding of course concepts and different perspectives.
Show steps
Show steps
Show steps
- Participate in ongoing discussions
- Ask clarifying questions
- Share insights and experiences
Practice Building a Mixed Effects Model in R
Show steps
Solidify your understanding of mixed effects models by building one yourself in R.
Browse courses on
Mixed Effects Models
Show steps
Show steps
Show steps
- Review the concepts of mixed effects models.
- Choose a dataset to analyze.
- Build the mixed effects model in R.
- Interpret the results of the model.
Create a visual representation of the forecasting model
Show steps
Visualizing your forecasting model will help you gain insights into its structure and assumptions, and identify potential areas for improvement.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose appropriate visualization techniques
- Create visualizations using data analysis tools
- Interpret and analyze the visualizations
Build a mock election forecasting model
Show steps
Creating a mock forecasting model will allow you to practice applying the concepts learned in the course and gain hands-on experience in election forecasting.
Browse courses on
Mixed Effects Models
Show steps
Show steps
Show steps
- Gather historical election data
- Choose appropriate mixed effects model
- Estimate model parameters
- Validate model performance
- Interpret and present results
Career center
Learners who complete Forecasting US Presidential Elections with Mixed Models will develop knowledge and skills
that may be useful to these careers:
Political Consultant
Political Consultant
Political Consultants analyze political environments, candidates, and issues to develop and implement campaign strategies and public relations campaigns. They may specialize in political polling, fundraising, or media relations. This course can help Political Consultants develop the skills needed to forecast election outcomes, which is essential for developing effective campaign strategies.
Election Analyst
Election Analyst
Election Analysts collect and analyze data on elections to forecast outcomes and understand voting trends. They may work for political parties, campaigns, or media outlets. This course would be particularly helpful for Election Analysts because it provides a strong foundation in mixed effects models, which are used to forecast election outcomes.
Data Scientist
Data Scientist
Data Scientists use statistical and computational methods to analyze and interpret data. They may work in a variety of industries, including finance, healthcare, and marketing. This course may be helpful for Data Scientists who want to specialize in political forecasting or who want to develop a foundation in mixed effects models.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to draw conclusions and make predictions. They may work in a variety of industries, including research, government, and business. This course may be helpful for Statisticians who want to specialize in political forecasting or who want to develop a foundation in mixed effects models.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use statistical and mathematical models to analyze and interpret data for investment decisions. They may work for hedge funds, investment banks, or other financial institutions. This course may be helpful for Quantitative Analysts who want to specialize in political forecasting or who want to develop a foundation in mixed effects models.
Risk Analyst
Risk Analyst
Risk Analysts identify, assess, and manage risks for organizations. They may work in a variety of industries, including finance, insurance, and healthcare. This course may be helpful for Risk Analysts who want to specialize in political risk or who want to develop a foundation in mixed effects models.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risks and make financial projections. They may work for insurance companies, pension funds, or other financial institutions. This course may be helpful for Actuaries who want to specialize in political risk or who want to develop a foundation in mixed effects models.
Financial Analyst
Financial Analyst
Financial Analysts analyze and interpret financial data to make investment recommendations. They may work for investment banks, hedge funds, or other financial institutions. This course may be helpful for Financial Analysts who want to specialize in political risk or who want to develop a foundation in mixed effects models.
Market Researcher
Market Researcher
Market Researchers conduct research to understand consumer trends and behavior. They may work for a variety of organizations, including marketing firms, advertising agencies, and product development companies. This course may be helpful for Market Researchers who want to specialize in political polling or who want to develop a foundation in mixed effects models.
Poller
Poller
Pollers conduct surveys to collect data on public opinion and political preferences. They may work for political parties, campaigns, or media outlets. This course would be particularly helpful for Pollers because it provides a strong foundation in mixed effects models, which are used to analyze polling data.
Campaign Manager
Campaign Manager
Campaign Managers oversee all aspects of political campaigns, including fundraising, advertising, and voter outreach. This course would be particularly helpful for Campaign Managers because it provides a strong foundation in mixed effects models, which can be used to forecast election outcomes and develop effective campaign strategies.
Public relations manager
Public relations manager
Public Relations Managers develop and implement public relations campaigns to build and maintain a positive image for their clients. This course may be helpful for Public Relations Managers who want to specialize in political public relations or who want to develop a foundation in mixed effects models.
Journalist
Journalist
Journalists write, edit, and report news stories for newspapers, magazines, and online publications. This course may be helpful for Journalists who want to specialize in political reporting or who want to develop a foundation in mixed effects models.
Political Scientist
Political Scientist
Political Scientists study political systems and behavior. They may work in academia, government, or non-profit organizations. This course would be particularly helpful for Political Scientists who want to specialize in electoral politics or who want to develop a foundation in mixed effects models.
Teacher
Teacher
Teachers educate students at all levels, from preschool through college. This course may be helpful for Teachers who want to teach about political science or who want to develop a foundation in mixed effects models.
For more career information including salaries, visit:
OpenCourser.com/course/agy3ln/forecasting
Reading list
We've selected 15 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Forecasting US Presidential Elections with Mixed Models.
Classic textbook on Bayesian data analysis. It covers a wide range of topics, from the basics of Bayesian inference to more advanced topics, such as hierarchical models and MCMC methods.
Save
Provides a comprehensive overview of forecasting methods. It covers a wide range of topics, from the basics of time series analysis to more advanced topics, such as state space models and machine learning methods.
Classic textbook on statistical learning. It covers a wide range of topics, from the basics of linear regression to more advanced topics, such as support vector machines and random forests.
Save
Provides a practical guide to predictive modeling. It covers a wide range of topics, from the basics of data preparation to more advanced topics, such as model selection and evaluation.
Provides a gentle introduction to Bayesian statistics and modeling using R and Stan. It covers a wide range of topics, from the basics of Bayesian inference to more advanced topics, such as hierarchical models and MCMC methods..
Provides a practical guide to Bayesian data analysis using R, JAGS, and Stan. It covers a wide range of topics, from the basics of Bayesian inference to more advanced topics, such as hierarchical models and MCMC methods.
Provides a comprehensive overview of regression modeling strategies. It covers a wide range of topics, from the basics of linear regression to more advanced topics, such as logistic regression and survival analysis.
Provides a comprehensive overview of generalized linear models. It covers a wide range of topics, from the basics of generalized linear models to more advanced topics, such as model selection and evaluation.
Provides a practical guide to machine learning. It covers a wide range of topics, from the basics of machine learning to more advanced topics, such as deep learning and neural networks.
Save
Provides a comprehensive overview of deep learning. It covers a wide range of topics, from the basics of deep learning to more advanced topics, such as convolutional neural networks and recurrent neural networks.
Provides a comprehensive overview of reinforcement learning. It covers a wide range of topics, from the basics of reinforcement learning to more advanced topics, such as deep reinforcement learning and multi-agent reinforcement learning.
Provides a comprehensive overview of natural language processing. It covers a wide range of topics, from the basics of natural language processing to more advanced topics, such as machine translation and text summarization.
Save
Provides a comprehensive overview of computer vision. It covers a wide range of topics, from the basics of computer vision to more advanced topics, such as object recognition and image segmentation.
Provides a comprehensive introduction to Bayesian statistics and modeling using R. It covers the basics of Bayesian inference, including prior distributions, likelihood functions, and posterior distributions. It also discusses more advanced topics, such as Markov chain Monte Carlo (MCMC) methods.
Provides a comprehensive overview of machine learning for predictive data analytics. It covers a wide range of topics, from the basics of machine learning to more advanced topics, such as deep learning and big data.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/agy3ln/forecasting
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Vinod Bakthavachalam
Language
English
Good to know
Provides knowledge in forecasting US Presidential Elections
Instructors are Vinod Bakthavachalam
Reviews electoral college selection of Presidents
Uses mixed effects models for prediction
Involves the R programming language
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