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!
ID
The course needs more exercises to practice R! Good Professors! Clear and Friendly expositions, thanks a lot!
AO
would have helped if there were even a glance about logistic with multiple outcomes
FG
Awesome course and looking forwards to dive into more Statistical analysis
RR
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.
CM
Very basic but would be useful for those unfamiliar with logistic regression in R
QY
That would be greater to use more examples to demonstrate the analysis of model fit. Overall this course is nice.
RP
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.
MA
This is one of the best courses. Dr. Alex is amazing and delivers the content quite well.
SS
Great course! All Life science students and those currently working in Data science& Clinical development R&D should take this course
ZK
An excellent introductory course. I hope they offer an advanced version of this specialization as well.
MM
some parts were harder to understand and I thought it needs more examples. but generally a very nice course and a very nice instructor.
TG
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.
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Great course! All Life science students and those currently working in Data science& Clinical development R&D should take this course
This is one of the best courses. Dr. Alex is amazing and delivers the content quite well.
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
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
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.
I would prefer also videos on how to interpret and work with Rstudio. Programming itself is a boring skill, reading about it makes it more boring. Therefore element of video about interpretation and use of formulas would definitely help to make it more interesting. Thanks for the great course
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.
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.
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.
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.
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).
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.
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.
This course provides very great guidance, as the other courses in this series do. Will definitely recommend this course for beginner programmers who want to delve into R programming specialising in public health.
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.
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.
Excelente curso, muy didáctico, intuitivo, aplicado y concentra el esfuerzo en la utilidad del modelo asi como los consejos antes algunos de los problemas de análisis y ajustes.
I love the technical skills I have learned in R and that the instructor did a great job explaining the concepts without bogging down in the details. Great course for beginners!
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.
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.