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.
RC
Nice course for the beginner who is pursuing health research and its multivariate analysis. It would be better if it is provided more elaborately in video lectures.
SI
The course was an excellent utilisation of time. I am looking forward to explore further and utilise the skills I acquired.
AO
This is is an excellent course! Thank you for providing it to us online, and please, I look forward to have access to more advance courses on statistical analysis for public health from ICL!
JA
Great step by step explanation of the linear regression model-building process. Very clear. Also highlights pitfalls to avoid.
JN
Excellent course! I liked the guided activities however there were some do-it-yourself activities that were required before learning of the necessary code.
PG
This was a wonderful course, for many reasons, the best of which was I felt as if I was finally getting into a real-world data analysis situation. I recommend it highly.
LB
I learned so much - definitely met learning objectives in terms of statistics and use of R for regression modeling!
NK
This is the best course to get started with linear regression and R as the instructor explains step by step on each strategy
AN
I learned a lot from the course even if a lot of areas felt unpolished and glossed over. Definitely recommend it.
EY
Superb - learnt how to model linear regression and multivariate regression in R. The summaries were excellent.
FG
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.
RE
Excellent online course with plenty of learning to now take away and apply to other datasets to enhance this learning.
显示：20/112
Wonderful course. Anyone with any background can attend this course. The general idea of regression you will get from here can be applied in any academic domain.
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
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.
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
With no prior background in programming and computational analysis, I have found this course as best starter to gain fundamental skills that will help me excel in my current field of life sciences. This course will surely help me in writing meta-analysis and doing statistical analysis of my research projects with a little bit guidance from statisticians rather than relying on statisticians to do data analysis part of my research design. Thanks Imperial College London, the instructors and Coursera to bring this brilliant specialization to me.
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.
Excellent course. We get a lot of hands on training in building regression models and crystal clear concepts.
Excellent course, with great classes and a large data set for you to test your computational skills.
Perhaps, the best linear regression course available online! Great job!
Reading materials and practicals are a bit messy. Poorly formatted, lack of logical structure, and low res screenshots of code and R console outputs (some of which code is buggy). Please provide the code that we can actually run, as R or Rmd files. Lack of quality unfortunately is not to professional or academic standard, you would be expected to produce a lot better on the job or for assignments so it's not a good example to set here. I hope the course content for on campus students is better than this. I imagine it is, so it just goes to show this was thrown together without any real care or quality control.
This was a great course! I have been stuck on the analysis of my research data and was looking for some kind of course which can help me develop an intuition how to build models, what makes sense clinically and statistically. This course exceeded my expectations. It provides short videos, helpful readings as well as exercises to implement the presented concepts. It helped me develop a structure how to approach the analysis of a data set and what is the good practice in selecting variables and building your model! The course was not only very informative but also very enjoyable.
As my first formal foray to R, I highly appreciate the hands-on approach of this course. While I have been aware and using linear regression for various research endeavors prior to this course, the material has provided me a more technical perspective on the advantages, assumptions and limitations of this statistical technique. It is extremely difficult to get decent scores in the quiz without actually trying R, so be prepared to devote more time and effort than you normally would in a MOOC.
I really enjoyed this course, which broke down the steps of how to build a multivariable regression model, from examining individual data, exploring its correlations, fitting a linear regression model, coding all of these in R, and even provided tips on how to put all of these together in a research paper. I don't have any delusions of being a data analyst after taking this course, but it gives me the confidence to take on a project and do a decent job of it.
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 :)
fantastic!
unlike the previous course from the same specialization which was a little bit fundamental, this course was so comprehensive that everyone can easily learn from basics to advanced levels of regression, along with all mistakes scientists may make when doing a regression thank you so much for the course
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.
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!
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.
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!
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.