Name: Logistic Regression in R for Public Health
Brand: Imperial College London
Availability: OnlineOnly
Rating: 4.8 (76 reviews)

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学生对 Imperial College London 提供的 Logistic Regression in R for Public Health 的评价和反馈

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课程概述

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!...

51 - Logistic Regression in R for Public Health 的 75 个评论（共 76 个）

创建者 qianmengxiao

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Apr 13, 2020

I like this course!The prof is good

创建者 Shova P

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Jul 15, 2019

Course is very easy to follow

创建者 Don E

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Jul 30, 2020

AWESOME. Very organized.

创建者 Jin C

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Jul 15, 2020

Thank you

You are the best!

创建者 Ning D

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Jul 27, 2019

very recommendable course

创建者 JOEL C H M

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Aug 2, 2020

It was so useful

创建者 fabien M

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Apr 19, 2020

Very interesting

创建者 Yasna P S

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Mar 4, 2020

Excellent course

创建者 Martina

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Jul 27, 2021

Amazing course!

创建者 TANG

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Oct 30, 2019

Very helpful!

创建者 MAJEDA M

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Nov 16, 2021

very good

创建者 Shakil A S

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Feb 17, 2021

Amazing!

创建者 Sidney d S P B

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Jun 28, 2020

Perfect!

创建者 수진

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Jul 12, 2020

Good!!

创建者 Hector P

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Oct 1, 2020

I enjoyed this course, although I wish they will have more detailed examples. Some lectures are quite long. But in general the instructor is quite clear and the course is well designed. Now it is my time to practice.

创建者 Vaishnavi N

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Jul 13, 2020

This is a great course though it was very challenging.It may take enough time for you to understand each concept clearly, but i think it is worth learning.

创建者 Mohamed G M

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Aug 24, 2020

some parts were harder to understand and I thought it needs more examples. but generally a very nice course and a very nice instructor.

创建者 Ahmed M Y O

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Sep 12, 2019

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

创建者 Carya M

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Jan 28, 2024

Very basic but would be useful for those unfamiliar with logistic regression in R

创建者 Shaukat A

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Sep 16, 2020

Very good course

创建者 Jean-Philippe M

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Jan 20, 2021

Great course!

创建者 Debasish K

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Aug 3, 2020

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.