Linear Regression in R for Public Health
Statistical Analysis with R for Public Health,
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.
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| Rating | 4.9★ based on 23 ratings |
|---|---|
| Length | 5 weeks |
| Effort | 4 weeks of study 3-5 hours per week |
| Starts | Jul 3 (46 weeks ago) |
| Cost | $49 |
| From | Imperial College London via Coursera |
| Instructors | Alex Bottle, Victoria Cornelius |
| Download Videos | On all desktop and mobile devices |
| Language | English |
| Subjects | Science Data Science Mathematics |
| Tags | Life Sciences Data Science Probability And Statistics Public Health |
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What people are saying
public health practitioner today
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.
conceptual rather than mathematical
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.
already quite an experienced
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.
excellent coursera content provided
Excellent course An excellent Coursera content provided from such a renowned faculty with so much organized and systematic instructions.
my only difficulty
extra browny points
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.
fun at 2x
Charming and really fun at 2x 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.
know whether or
In summary if you are reading this chances are you want to know whether or not to do this course.
questions being wrong
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).
best wishes
Best wishes to Imperial faculty for offering such a great course.
do it
DO IT The end Perhaps, the best linear regression course available online!
good examples
Good examples.
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| Rating | 4.9★ based on 23 ratings |
|---|---|
| Length | 5 weeks |
| Effort | 4 weeks of study 3-5 hours per week |
| Starts | Jul 3 (46 weeks ago) |
| Cost | $49 |
| From | Imperial College London via Coursera |
| Instructors | Alex Bottle, Victoria Cornelius |
| Download Videos | On all desktop and mobile devices |
| Language | English |
| Subjects | Science Data Science Mathematics |
| Tags | Life Sciences Data Science Probability And Statistics Public Health |
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