Back to Logistic Regression in R for Public Health
Imperial College London

Logistic Regression in R for Public Health

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!

Status: Statistical Modeling
Status: Model Evaluation
IntermediateCourse12 hours

Featured reviews

ID

5.0Reviewed Jan 24, 2022

The course needs more exercises to practice R! Good Professors! Clear and Friendly expositions, thanks a lot!

AO

4.0Reviewed Sep 11, 2019

would have helped if there were even a glance about logistic with multiple outcomes

PL

5.0Reviewed Sep 27, 2020

Overall, it is good. But the feedback of the quiz was sometimes not helpful. Few explanation so that I was struggling to get the right answers.

RR

5.0Reviewed Dec 23, 2020

This is a wonderful course. Anyone who wants to model a binary classification model must go for this course. It covers everything in details with logic and humour.

AD

5.0Reviewed Jul 19, 2020

Excellent course. To the point explanations with a good sense of humour.

RP

5.0Reviewed Dec 18, 2020

Very good specialisation on logistic regression, with depth info not only on how-to of the model creation itself, but interpreting and choosing between multiple ones. I fully recommend it.

SS

5.0Reviewed Apr 10, 2020

Great course! All Life science students and those currently working in Data science& Clinical development R&D should take this course

SA

5.0Reviewed Mar 29, 2020

Very valuable information presented in a very clear way. It was super useful to me. Thanks!

SP

5.0Reviewed Oct 17, 2019

Amazing course. I'm looking forward to the survival analysis course. Week 3 is specially good. I'm sure you'll have fun.

MA

5.0Reviewed Mar 31, 2019

This is one of the best courses. Dr. Alex is amazing and delivers the content quite well.

FG

5.0Reviewed Jan 18, 2020

Awesome course and looking forwards to dive into more Statistical analysis

CM

4.0Reviewed Jan 27, 2024

Very basic but would be useful for those unfamiliar with logistic regression in R

All reviews

Showing: 20 of 76

Sajith Sasidharan
5.0
Reviewed Apr 11, 2020
Moses Banda Aron
5.0
Reviewed Apr 1, 2019
Nevin John
5.0
Reviewed Dec 5, 2019
LIANG Ying
5.0
Reviewed Aug 22, 2020
Ollie Dean
5.0
Reviewed Aug 27, 2020
kasra khalifehpour
5.0
Reviewed Apr 28, 2021
SAVINO SANDRO
5.0
Reviewed Sep 29, 2020
Wei Qi Loh
5.0
Reviewed Aug 31, 2020
Mohammad Rafiq Wani
5.0
Reviewed Nov 18, 2019
Arijit Nag
5.0
Reviewed Dec 3, 2019
Vivekananda Das
5.0
Reviewed Jun 19, 2019
Ma. Guadalupe Guadarrama Huerta
5.0
Reviewed Mar 9, 2020
Ikenna Mbagwu
5.0
Reviewed Jan 23, 2020
C J
5.0
Reviewed Jul 28, 2024
Erin
5.0
Reviewed Nov 12, 2019
Roxana Popa
5.0
Reviewed Dec 19, 2020
OSCAR GAMBOA RIVEROS
5.0
Reviewed Sep 5, 2023
Nhi Lang
5.0
Reviewed Dec 31, 2022
Rahul Raoniar
5.0
Reviewed Dec 24, 2020
Tommy Gonzalez
5.0
Reviewed Sep 10, 2019