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學生對 伦敦帝国学院 提供的 Logistic Regression in R for Public Health 的評價和反饋

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265 個評分
57 條評論

課程概述

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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RP
2020年12月18日

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.

RR
2020年12月23日

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.

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51 - Logistic Regression in R for Public Health 的 56 個評論(共 56 個)

創建者 Ahmed M Y O

2019年9月12日

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

創建者 Shaukat A

2020年9月16日

Very good course

創建者 Jean-Philippe M

2021年1月20日

Great course!

創建者 Debasish K

2020年8月3日

Pros: (1) A great effort to give an understanding of fit, prediction and commands in R. (2) It covers important commands.

Cons: (1) The content was vague at times. Too much was left for the lecturer to tell and not enough visualizations provided in the video. Like what you do with data from the start of the EDA journey (There could have been a cheat sheet on the relation between the values (coefficients, p-values, etc).

Two areas of improvements: (1) Use more visualiations in the lecture. Like explaining the summary outout in R. Perhaps there were technical odds of doing it in the video and that got translated into Reading material. (2) The Reading Material is not sequenced well. So, consider 'residual deviation' and 'deviance residual'. What is the difference? I had to go to https://rpubs.com/fhernanb/deviance_glm to understand without any difficulty. As opposed to a lot of text in the Reading Materials, a notebook approach would be better. Attempt was made but it lacked the clarity of Rpubs. (3) The Practice Quiz was very easy while the multiple choice in the Final Quiz was baffling (since the Reading material was not adequate).

Thank you. Hope this helps.

創建者 Rishi j

2019年12月3日

Videos in the course were of no use

創建者 Kedeen O

2021年1月13日

I had a problem with running the logistic regression in R since the missing observations that R gave me was not the same as given in this course. Therefore, due to missing observations in R not being the same as the missing values stated in these modules, I had a hard time answering some of the quiz, particularly Week 3. I requested for help but I did not get a response, I eventually solved the problem but going into Excel and cleaning up my data.