Logistic Regression in R for Public Health(으)로 돌아가기

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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!...

Sep 28, 2019

This one is better compared with the one about linear regression regarding the quizzes, which are designed better to test your knowledge

Apr 01, 2019

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

필터링 기준:

교육 기관: ji t

•Apr 05, 2019

very good course!! highly recommend!! Although I am not major in public health, I learned a lot about logistic regression and basic ideas for data science

교육 기관: Moses C B A

•Apr 01, 2019

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

교육 기관: Vivekananda D

•Jun 19, 2019

Excellent course! Highly recommended for people who want an introduction to Logistic Regression. I hope the instructor offers another version of the course with little more advanced material (for example, ordinal and multinomial logit models).

교육 기관: Shova P

•Jul 15, 2019

Course is very easy to follow

교육 기관: Ning D

•Jul 27, 2019

very recommendable course

교육 기관: Tommys J G G

•Sep 10, 2019

Excellent and very complete course on R. Specially for those working in public health and with an interest in understanding models of clinical trials, etc.

교육 기관: Donghan S

•Sep 28, 2019

This one is better compared with the one about linear regression regarding the quizzes, which are designed better to test your knowledge

교육 기관: Sergio P

•Oct 18, 2019

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

교육 기관: TANG

•Oct 31, 2019

Very helpful!

교육 기관: Erin

•Nov 12, 2019

An excellent way to get oriented to Logistic Regression in R! The course is created with a particular nod to public health, but nearly everything was still relevant to my own research in health psychology.

교육 기관: MOHAMMAD R W

•Nov 19, 2019

I must thank the instructors and Coursera for this course. I have become more confident in using R for data analysis. The course helps you to understand when and when not to use logistic regression for your data. That is important for me as a Biology PhD student.

교육 기관: Ahmed M Y O

•Sep 12, 2019

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