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Logistic Regression in R for Public Health
  • Level Professional
  • Duration 12 hours
  • Course by Imperial College London
  • Offered by

About

Welcome to Logistic Regression in R for Public Health! Why logistic regression for public health rather than just logistic regression? Well, there are some particular considerations for every data set, and public health data sets have particular features that need special attention. In a word, they're messy. Like the others in the series, this is a hands-on course, giving you plenty of practice with R on real-life, messy data, with predicting who has diabetes from a set of patient characteristics as the worked example for this course. Additionally, the interpretation of the outputs from the regression model can differ depending on the perspective that you take, and public health doesn’t just take the perspective of an individual patient but must also consider the population angle. That said, much of what is covered in this course is true for logistic regression when applied to any data set, so you will be able to apply the principles of this course to logistic regression more broadly too. By the end of this course, you will be able to: Explain when it is valid to use logistic regression Define odds and odds ratios Run simple and multiple logistic regression analysis in R and interpret the output Evaluate the model assumptions for multiple logistic regression in R Describe and compare some common ways to choose a multiple regression model This course builds on skills such as hypothesis testing, p values, and how to use R, which are covered in the first two courses of the Statistics for Public Health specialisation. If you are unfamiliar with these skills, we suggest you review Statistical Thinking for Public Health and Linear Regression for Public Health before beginning this course. If you are already familiar with these skills, we are confident that you will enjoy furthering your knowledge and skills in Statistics for Public Health: Logistic Regression for Public Health. We hope you enjoy the course!

Modules

Welcome to Imperial College London

    • 5 Readings
    • 1 Discussion
    • 1 Plugin
Show info about module content

1 Discussions

  • Nice to meet you!

5 Readings

  • About Imperial College & the team
  • How to be successful in this course
  • Grading policy
  • Data set and Glossary
  • Additional Reading

Introduction to Logistic Regression

    • 3 Videos
    • 2 Readings
    • 1 Discussion
    • 2 Assignment
Show info about module content

2 Assignment

  • Logistic Regression
  • End of Week Quiz

1 Discussions

  • What’s Your Experience of Logistic Regression?

3 Videos

  • Welcome to the Course
  • Introduction to Logistic Regression
  • Odds and Odds Ratios

2 Readings

  • Why does linear regression not work with binary outcomes?
  • Odds Ratios and Examples from the Literature

Logistic Regression in R

    • 2 Videos
    • 4 Readings
    • 1 Discussion
    • 2 Assignment
Show info about module content

2 Assignment

  • Cross Tabulation
  • Interpreting Simple Logistic Regression

1 Discussions

  • Share and Reflect: Results of the Simple Logistic Regression

2 Videos

  • Preparing the Data For Logistic Regression
  • Logistic Regression in R

4 Readings

  • How to Describe Data in R
  • Results of Cross Tabulation
  • Practice in R: Simple Logistic Regression
  • Feedback - Output and Interpretation from Simple Logistic Regression

Multiple Logistic Regression in R

    • 1 Videos
    • 6 Readings
    • 2 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Running A New Logistic Regression Model

2 Discussions

  • Share and Reflect: Describing Variables and R Analyses
  • Share and Reflect: What do the regression results mean?

1 Videos

  • How to Run Multiple Logistic Regression in R

6 Readings

  • Describing your Data and Preparing to Run Multiple Logistic Regression
  • Practice in R: Describing Variables
  • Feedback
  • Practice in R: Running Multiple Logistic Regression
  • Feedback: Multiple Regression Model
  • Feedback on the Assessment

Assessing Model Fit

    • 3 Videos
    • 10 Readings
    • 1 Discussion
    • 1 Plugin
    • 3 Assignment
Show info about module content

3 Assignment

  • Quiz on R’s Default Output for the Model
  • Overfitting and Model Selection
  • End of Course Quiz

1 Discussions

  • Share and Reflect: Results from the New Model

3 Videos

  • Choosing a Logistic Regression Model
  • Overfitting and Non-convergence
  • Summary of the Course

10 Readings

  • Model Fit in Logistic Regression
  • How to Interpret Model Fit and Performance Information in R
  • Further Reading on Model Fit
  • Summary of Different Ways to Run Multiple Regression
  • Practice in R: Applying Backwards Elimination
  • Feedback: Backwards Elimination
  • Practice in R: Run a Model with Different Predictors
  • Feedback on the New Model
  • Further Reading on Model Selection Methods
  • R Code for the Whole Module

Auto Summary

Discover how to effectively apply logistic regression in public health with this hands-on course. Taught by Coursera, it focuses on real-life messy data to predict diabetes from patient characteristics, emphasizing the unique considerations in public health datasets. The course covers odds ratios, model assumptions, and multiple regression analysis in R. Ideal for professionals, the course spans 720 minutes and offers Starter and Professional subscription options. Prior knowledge of hypothesis testing and p-values is recommended to maximize learning. Enhance your statistical skills for public health today!

Alex Bottle
Instructor

Alex Bottle