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

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.

필터링 기준:

교육 기관: Nevin J

•Dec 05, 2019

Excellent course. Good for those with solid understanding of basic statistics but looking to implement logistic regression in analysis using R. Needs decent understanding of R. It takes you through the basics of logistic regression. It explains really well using analogies and examples. It explains things well without getting stuck in the mathematical background too much. The quizzes are great and the feedback through course outstanding

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: Arijit N

•Dec 03, 2019

Clinically relevant and lucid discussions.

Thoroughly recommended for medical professionals who are not highly skilled in mathematical analysis and need simple statements and exercises to understand the basic concepts.

Very good for beginners.

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: Maria G G H

•Mar 10, 2020

Excelente curso, me cuesta un poco de trabajo por que no soy hablante del Inglés, sin embargo, tanto las tareas como los ejercicios están muy bien planeados para asegurar el aprendizaje y mantener el interés hasta su conclusión.

교육 기관: Ikenna M

•Jan 24, 2020

Excellent course and I will highly recommend it to other people seeking to gain knowledge of Logistic Regression. However, there were some typographical errors, which I believe will be corrected by a quality control team.

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: 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: 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: rob v m

•Mar 24, 2020

Excellent course on logistic regression. I especially appreciated the R code exercises given and the clear videos presented by Dr. Alex Bottle

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: Pau G C

•Mar 03, 2020

A good overview of Logistic Regression from zero.

A very useful tool for public health data

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: Fidel G

•Jan 19, 2020

Awesome course and looking forwards to dive into more Statistical analysis

교육 기관: yi j

•Feb 02, 2020

Very practical and explicit course about logistic regression.

교육 기관: Enrique L

•Mar 27, 2020

Really good course!! Highly recommended.

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: Yasna P S

•Mar 04, 2020

Excellent course

교육 기관: TANG

•Oct 31, 2019

Very helpful!

교육 기관: Ahmed M Y O

•Sep 12, 2019

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

교육 기관: Rishi J

•Dec 03, 2019

Videos in the course were of no use