Home > Courses > Logistic Regression in R for Public Health
4.7 25 reviews on Coursera
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.
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
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!
4.7
74 Ratings
Free
per course
Incentives
100% online
Course 3 of 4 in the
Flexible deadlines
Intermediate Level
Approx. 11 hours to complete
English
Also check at
FAQs About "Logistic Regression in R for Public Health"
Is the online course "Logistic Regression in R for Public Health" free?
Yes, "Logistic Regression in R for Public Health" is a free online course offered on the online classes platform Coursera
Is "Logistic Regression in R for Public Health" an online course worth taking?
25 students from the online classes platform Coursera have given an average rating of 4.7 to the online "Logistic Regression in R for Public Health" and at least 2657 have completed this online training.
About
Elektev is on a mission to organize educational content on the Internet and make it easily accessible. Elektev provides users with online course details, reviews and prices on courses aggregated from multiple online education providers. DISCLOSURE: This page may contain affiliate links, meaning when you click the links and make a purchase, we receive a commission.