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Logistic Regression in R for Public Health

Approx. 12 hours to complete

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Course Summary

Learn how to use logistic regression in R for public health applications in this comprehensive course. You will gain hands-on experience through real-world examples and datasets.

Key Learning Points

Understand the basics of logistic regression and its applications in public health
Learn how to use R software for logistic regression analysis
Gain practical skills through real-world examples and datasets

Learning Outcomes

Understand the theory and practice of logistic regression
Gain proficiency in R software for data analysis
Apply logistic regression in public health applications

Prerequisites or good to have knowledge before taking this course

Basic knowledge of statistics
Familiarity with R programming

Course Difficulty Level

Intermediate

Course Format

Online Course
Self-Paced
Hands-On

Similar Courses

Data Analysis and Statistical Inference
Data Science Essentials

Related Education Paths

Notable People in This Field

Dr. Andrew Gelman
Dr. Hadley Wickham

Related Books

Description

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!

Knowledge

Describe a data set from scratch using descriptive statistics and simple graphical methods as a first step for advanced analysis using R software
Interpret the output from your analysis and appraise the role of chance and bias as potential explanations
Run multiple logistic regression analysis in R and interpret the output
Evaluate the model assumptions for multiple logistic regression in R

Outline

Introduction to Logistic Regression
Welcome to the Course
Introduction to Logistic Regression
Odds and Odds Ratios
About Imperial College & the team
How to be successful in this course
Grading policy
Data set and Glossary
Additional Reading
Why does linear regression not work with binary outcomes?
Odds Ratios and Examples from the Literature
Logistic Regression
End of Week Quiz

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

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

Assessing Model Fit
Choosing a Logistic Regression Model
Overfitting and Non-convergence
Summary of the Course
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
Quiz on R’s Default Output for the Model
Overfitting and Model Selection

Summary of User Reviews

Discover the power of logistic regression in public health with this Coursera course. Learn how to apply this statistical tool to real-world problems and make data-driven decisions. Overall, users found this course informative and engaging.

Key Aspect Users Liked About This Course

Many users appreciated the practical examples and case studies used throughout the course, which helped them understand how to apply logistic regression in real-world scenarios.

Pros from User Reviews

Practical examples and case studies help users apply their knowledge to real-world scenarios
Instructors are knowledgeable and responsive to questions
Course content is well-organized and easy to follow
Quizzes and assignments provide useful feedback to reinforce learning

Cons from User Reviews

Some users found the course challenging and wished for more guidance on certain topics
Occasional technical issues with the Coursera platform
Some users felt that the pace of the course was too slow or too fast for their learning style
Some users found the course too basic and wished for more advanced content

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