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Вернуться к Logistic Regression in R for Public Health

Отзывы учащихся о курсе Logistic Regression in R for Public Health от партнера Имперский колледж Лондона

Оценки: 225
Рецензии: 49

О курсе

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!...

Лучшие рецензии


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


Apr 11, 2020

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

Фильтр по:

26–48 из 48 отзывов о курсе Logistic Regression in R for Public Health

автор: Joseph L

Aug 28, 2020

Good, easy to follow introduction to logistic regression

автор: Dhan K B

Oct 19, 2020

I learned regression in R through this course

автор: Victor I M

Sep 24, 2020

Excellent demonstrations and explanations.

автор: Shek L T

May 15, 2020

Excellent teaching! very useful R codes!

автор: Enrique L

Mar 27, 2020

Really good course!! Highly recommended.

автор: qianmengxiao

Apr 14, 2020

I like this course!The prof is good

автор: Shova P

Jul 15, 2019

Course is very easy to follow

автор: Don A E

Jul 31, 2020

AWESOME. Very organized.

автор: Jin C

Jul 15, 2020

Thank you

You are the best!

автор: Ning D

Jul 27, 2019

very recommendable course

автор: JOEL C H M

Aug 02, 2020

It was so useful

автор: fabien M

Apr 19, 2020

Very interesting

автор: Yasna P S

Mar 04, 2020

Excellent course

автор: TANG

Oct 31, 2019

Very helpful!

автор: Sidney d S P B

Jun 28, 2020


автор: Kim S J

Jul 13, 2020


автор: Hector P

Oct 02, 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.

автор: Vaishnavi N

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.

автор: Mohamed G M

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.

автор: Ahmed M Y O

Sep 12, 2019

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

автор: Shaukat A

Sep 16, 2020

Very good course

автор: Debasish K

Aug 03, 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 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.

автор: Rishi J

Dec 03, 2019

Videos in the course were of no use