Chevron Left
Retour à Logistic Regression in R for Public Health

Avis et commentaires pour d'étudiants pour Logistic Regression in R for Public Health par Imperial College London

324 évaluations

À propos du cours

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!...

Meilleurs avis


18 déc. 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.


23 déc. 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.

Filtrer par :

1 - 25 sur 66 Avis pour Logistic Regression in R for Public Health

par Sajith S

11 avr. 2020

par Nevin J

5 déc. 2019


22 août 2020

par Ollie D

27 août 2020

par kasra k

28 avr. 2021


29 sept. 2020

par Wei Q L

31 août 2020

par Mohammad R W

18 nov. 2019

par Arijit N

3 déc. 2019

par Vivekananda D

19 juin 2019

par Maria G G H

9 mars 2020

par Ikenna M

23 janv. 2020

par Erin

12 nov. 2019

par Roxana P

19 déc. 2020

par Rahul R

24 déc. 2020

par Tommy G

10 sept. 2019

par ji t

5 avr. 2019

par Pei-Yu L

28 sept. 2020

par Luna D R

17 août 2020

par Rob v M

24 mars 2020

par Donghan S

27 sept. 2019

par Elisabeth P

12 févr. 2021

par Paweł P

23 nov. 2022

par Sergio P

18 oct. 2019

par Ying Q

9 août 2022