Linear Regression in R for Public Health (으)로 돌아가기

# 임페리얼 칼리지 런던의 Linear Regression in R for Public Health 학습자 리뷰 및 피드백

4.8
별점
282개의 평가
58개의 리뷰

## 강좌 소개

Welcome to Linear Regression in R for Public Health! Public Health has been defined as “the art and science of preventing disease, prolonging life and promoting health through the organized efforts of society”. Knowing what causes disease and what makes it worse are clearly vital parts of this. This requires the development of statistical models that describe how patient and environmental factors affect our chances of getting ill. This course will show you how to create such models from scratch, beginning with introducing you to the concept of correlation and linear regression before walking you through importing and examining your data, and then showing you how to fit models. Using the example of respiratory disease, these models will describe how patient and other factors affect outcomes such as lung function. Linear regression is one of a family of regression models, and the other courses in this series will cover two further members. Regression models have many things in common with each other, though the mathematical details differ. This course will show you how to prepare the data, assess how well the model fits the data, and test its underlying assumptions – vital tasks with any type of regression. You will use the free and versatile software package R, used by statisticians and data scientists in academia, governments and industry worldwide....

## 최상위 리뷰

MK

Apr 04, 2020

This is an excellent course to learn how to think statistically with respect to linear regression. The course covers a lot of materials and equips one to further explore this vast area.

MW

Oct 04, 2019

The course was really great. The instructor explained the things in a lucid manner. Also the reading materials were great. Thank you so much for this course

필터링 기준:

## Linear Regression in R for Public Health 의 59개 리뷰 중 1~25

교육 기관: William E

Jul 12, 2019

This course is excellent- if you want a solid understanding of the basics, this is as good as it gets. I would say it is most suited for somebody who wants a more conceptual rather than mathematical understanding of the subject, but its still has a good balance between the both approaches. The videos are very well presented, the lecturer is very professional and has clear and engaging style [not like most stats teachers ;) ]. My only difficulty was that I am already quite an experienced R user and the coding methods were quite different to my style, that's not a criticism really as there are numerous ways to remove the outer layer of a feline, as they say. There a decent number of typos and I was a little frustrated with some of the answers to the questions being wrong (I was convinced on a couple of occasions that I had it right and they didn't). I'm not the expert so they were almost certainly right it's just the explanation to the answer didn't really help me understand. Also for extra browny points it would great if the R code was formatted in a codey way in the reading lesson- like in stackoverflow. It kind of gets lost in the text. In summary if you are reading this chances are you want to know whether or not to do this course. DO IT The end

교육 기관: Rashidul H

May 30, 2019

An excellent Coursera content provided from such a renowned faculty with so much organized and systematic instructions. I truly enjoyed the whole course to learn the concept and had ample opportunity with tasks to practice analysis skills with the provided example data. I would really recommend anyone to participate on this course. Best wishes to Imperial faculty for offering such a great course.

교육 기관: Rashmi M

Sep 22, 2019

Excellent course. We get a lot of hands on training in building regression models and crystal clear concepts.

교육 기관: Sergio P

Sep 20, 2019

Excellent course, with great classes and a large data set for you to test your computational skills.

교육 기관: Brenda Y

Jul 02, 2020

I loved the step-by-step approach and learned a lot in this course! I only wish that activities could be assessed (perhaps by peer review, as I have seen in other courses), so I could know if I am on the right track, or how to troubleshoot certain issues (still struggling with interaction terms...). Otherwise, definitely a great course with great materials teaching very useful skills :)

교육 기관: Kalyango E

Dec 21, 2019

Initially I was scared of R programming and statistics because i thought it was for data scientists, but this course was easy to follow and the exercises are rigorous. You come out of this course confident in your analytic skills. Wonderful teachers and thanks for sharing your knowledge.

교육 기관: SAVINO S

Sep 24, 2020

Interesting and well planned. It follows the path laid down in the previous course, alternating short videos and moments of personal reading and reflection, followed by feedbacks. Plus the chance to see some real data processing, both guided and do-it-yourself. Good!

교육 기관: Tommys J G G

Aug 15, 2019

Excellent course! Very hard in some aspects but very engaging and it provides students with deep knowledge of linear regression, epidemiology with R usage, and biostatistics skills which I consider essential for every Public Health Practitioner today.

교육 기관: Hector P

Sep 19, 2020

This is one of the best courses I have ever taken. Congratulations to the design team and the instructors. The level of this course is excellent and the feedback the instructor provided helped me to go further. I am so happy I took this course.

교육 기관: Swetha J

May 19, 2020

The course explained the intricacies of Linear Regression very well. esp. the interaction effect and addressing categorial variables and how to select variables, which is often overseen in most content/ courses. Excellent course!

교육 기관: Sabine f

Dec 10, 2019

In a matter of days I was able to understand linear regression using R. Great videos and homework assignments that are doable and can be applied directly to own research. This course is a must for any Phd student in healthcare.

교육 기관: Michael K

Apr 04, 2020

This is an excellent course to learn how to think statistically with respect to linear regression. The course covers a lot of materials and equips one to further explore this vast area.

Oct 04, 2019

The course was really great. The instructor explained the things in a lucid manner. Also the reading materials were great. Thank you so much for this course

교육 기관: Victor I M

Sep 09, 2020

Very good explanations, I have learnt a lot of statistics as a science student, but I learnt way more thanks to this course

교육 기관: Fidel G

Dec 23, 2019

Great course that takes you step by step on how to create model selection in R which you can be apply into the real world.

교육 기관: Rebecca E

Aug 17, 2020

Excellent online course with plenty of learning to now take away and apply to other datasets to enhance this learning.

교육 기관: Dawn D

Apr 30, 2020

Thorough explanation of linear regression, building on the basics right up to model building. Highly recommend! :)

교육 기관: Kelly B

Apr 21, 2020

The course was really well put together and fitted really nicely taught in the first course in the specialisation.

교육 기관: Mohd. A H

Jul 01, 2020

I learned a lot from the course even if a lot of areas felt unpolished and glossed over. Definitely recommend it.

교육 기관: Rodolfo I C S

Aug 27, 2019

Great Course. For a person wanting to learn coding from scratch it is very friendly and easy to understand.

교육 기관: Vũ M L

May 09, 2020

I love this course. I can understand clearly how linear regression work and apply this in real situation

교육 기관: Don A E

Jul 05, 2020

Great Refresher or first course. Videos were the right length. The difficulty the right level.

교육 기관: Jose L V V

Jan 27, 2020

good explanations about the utilities of simple and multiple linear regression in public health!!!

교육 기관: Pau G C

Feb 24, 2020

Good course to get familyar with linear regression from the very begining , Very useful lectures

교육 기관: Sunny M

Jun 27, 2020

The course offered a systematic and efficient way to learn linear regression in R.