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

임페리얼 칼리지 런던의 Logistic Regression in R for Public Health 학습자 리뷰 및 피드백

253개의 평가
53개의 리뷰

강좌 소개

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!...

최상위 리뷰

2020년 12월 18일

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.

2020년 12월 23일

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.

필터링 기준:

Logistic Regression in R for Public Health의 52개 리뷰 중 1~25

교육 기관: Sajith S

2020년 4월 11일

Great course! All Life science students and those currently working in Data science& Clinical development R&D should take this course

교육 기관: Nevin J

2019년 12월 5일

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: LIANG Y

2020년 8월 22일

I like Alex courses so much. He talks about the foundation of regression. Actually, I have done biostat for a few years, but still inspired by the basic theory of Biostatisitcs. I think no matter how long we have been in this industry or how far we have gone, all the things could not not be built up without solid foundation

교육 기관: Ollie D

2020년 8월 27일

Having already learn't the concepts of logistic regression in my final year at Sussex University, it was worthwhile applying the maths to a software like R. I now feel more prepared to apply for jobs in to the field of data science, especially in public health, where I'd like to go in to health modelling.

교육 기관: SAVINO S

2020년 9월 29일

As the previous courses of this specialization, i found this well built and informative. Some parts of the latter weeks overlap with those of the 2nd course of the specialisation, but i think that's unavoidable. Plenty of R to exercise with, maybe a bit too much by the end of the course.

교육 기관: Wei Q L

2020년 8월 31일

The instructor was very clear and succinct; I found it easy to follow. Having a sense of humor also helped. I have a good grasp on doing logistic regression with R now. I liked how stream-lined and focused the course was, which can't really be said for many others R courses.

교육 기관: Mohammad R W

2019년 11월 18일

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

2019년 12월 3일

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

2019년 6월 19일

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

2020년 3월 9일

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

2020년 1월 23일

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

2019년 11월 12일

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: Roxana P

2020년 12월 19일

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.

교육 기관: Rahul R

2020년 12월 24일

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.

교육 기관: Tommy G

2019년 9월 10일

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

2019년 4월 5일

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: Pei-Yu L

2020년 9월 28일

Overall, it is good. But the feedback of the quiz was sometimes not helpful. Few explanation so that I was struggling to get the right answers.

교육 기관: Denise R

2020년 8월 17일

Good. Explains right from the bottom. A little more of visualization would've been good. A correlation matrix between predictors, a ROC curve.

교육 기관: rob v m

2020년 3월 24일

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

2019년 9월 27일

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

2019년 10월 18일

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: Sara A L

2020년 3월 30일

Very valuable information presented in a very clear way. It was super useful to me. Thanks!

교육 기관: Pau G C

2020년 3월 3일

A good overview of Logistic Regression from zero.

A very useful tool for public health data

교육 기관: Moses C B A

2019년 4월 1일

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

교육 기관: Fidel G

2020년 1월 19일

Awesome course and looking forwards to dive into more Statistical analysis