Linear Regression in R for Public Health
- Offered byCoursera
Linear Regression in R for Public Health at Coursera Overview
Duration | 15 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Linear Regression in R for Public Health at Coursera Highlights
- 33% started a new career after completing these courses.
- 25% got a tangible career benefit from this course.
- Earn a shareable certificate upon completion.
Linear Regression in R for Public Health at Coursera Course details
- 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.
Linear Regression in R for Public Health at Coursera Curriculum
INTRODUCTION TO LINEAR REGRESSION
Welcome to the Course
Pearson?s Correlation Part I
Pearson?s Correlation Part II
Intro to Linear Regression: Part I
Intro to Linear Regression: Part II
Linear Regression and Model Assumptions: Part I
Linear Regression and Model Assumptions: Part II
About Imperial College London & the Team
How to be successful in this course
Grading policy
Data set and Glossary
Additional Reading
Linear Regression Models: Behind the Headlines
Linear Regression Models: Behind the Headlines: Written Summary
Warnings and precautions for Pearson's correlation
Introduction to Spearman correlation
Linear Regression Models: Behind the Headlines
Correlations
Spearman Correlation
Practice Quiz on Linear Regression
End of Week Quiz
Linear Regression in R
Introduction to Week 2
Fitting the linear regression
Multiple Regression
Recap on installing R
Assessing distributions and calculating the correlation coefficient in R
Feedback
How to fit a regression model in R
Feedback
Fitting the Multiple Regression in R
Feedback
Summarising correlation and linear regression
Linear Regression
End of Week Quiz
Multiple Regression and Interaction
Introduction to Key Dataset Features: Part I
Introduction to Key Dataset Features: Part II
Interactions between binary variables
Interactions between binary and continuous variables
How to assess key features of a dataset in R
How to check your data in R
Good Practice Steps
Practice with R: Run a Good Practice Analysis
Practice with R: Run Multiple Regression
Feedback
Practice with R: Running and interpreting a multiple regression
Feedback
Additional Reading
Fitting and interpreting model results
Interpretation of interactions
MODEL BUILDING
Intro to Model Development
Variable Selection
Developing a Model Building Strategy
Summary of developing a Model Building Strategy
Summary of Course
Feedback
Further details of limitations of stepwise
How many predictors can I include?
Practice with R: Developing your model
Practice with R: Fitting the final model
Feedback on developing the model
Final R Code
Problems with automated approaches
End of Course Quiz
Linear Regression in R for Public Health at Coursera Admission Process
Important Dates
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