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Über den Kurs

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!...

Top-Bewertungen

DS

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

SS

Apr 11, 2020

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

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26 - 36 von 36 Bewertungen für Logistic Regression in R for Public Health

von Ning D

Jul 27, 2019

very recommendable course

von JOEL C H M

Aug 02, 2020

It was so useful

von fabien M

Apr 19, 2020

Very interesting

von Yasna P S

Mar 04, 2020

Excellent course

von TANG

Oct 31, 2019

Very helpful!

von Sidney d S P B

Jun 28, 2020

Perfect!

von Kim S J

Jul 13, 2020

Good!!

von 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.

von Ahmed M Y O

Sep 12, 2019

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

von 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 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.

von Rishi J

Dec 03, 2019

Videos in the course were of no use