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Logistic Regression in R for Public Health

This course is a part of Statistical Analysis with R for Public Health, a 4-course Specialization series from Coursera.

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!

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Imperial College London

Rating 5.0 based on 4 ratings
Length 5 weeks
Effort 3-5 hours/week
Starts Jul 6 (last week)
Cost $49
From Imperial College London via Coursera
Instructor Alex Bottle
Download Videos On all desktop and mobile devices
Language English
Subjects Science Data Science Mathematics
Tags Life Sciences Data Science Probability And Statistics Public Health

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highly recommend in 2 reviews

highly recommend!!

about logistic regression in one review

Although I am not major in public health, I learned a lot about logistic regression and basic ideas for data science This is one of the best courses.

content quite well in one review

Dr. Alex is amazing and delivers the content quite well.

instructor offers another in one review

I hope the instructor offers another version of the course with little more advanced material (for example, ordinal and multinomial logit models).

little more advanced in one review

major in public in one review

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Coursera

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Imperial College London

Rating 5.0 based on 4 ratings
Length 5 weeks
Effort 3-5 hours/week
Starts Jul 6 (last week)
Cost $49
From Imperial College London via Coursera
Instructor Alex Bottle
Download Videos On all desktop and mobile devices
Language English
Subjects Science Data Science Mathematics
Tags Life Sciences Data Science Probability And Statistics Public Health

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