Linear Regression in 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.
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- Level Professional
- Duration 15 hours
- Course by Imperial College London
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Offered by
About
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.Modules
Welcome to Imperial College London
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5
Readings
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1
Discussion
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1
Plugin
1
Discussions
- Nice to meet you!
5
Readings
- About Imperial College London & the Team
- How to be successful in this course
- Grading policy
- Data set and Glossary
- Additional Reading
Linear Regression and Correlation
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7
Videos
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4
Readings
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1
Discussion
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5
Assignment
5
Assignment
- Linear Regression Models: Behind the Headlines
- Correlations
- Spearman Correlation
- Practice Quiz on Linear Regression
- End of Week Quiz
1
Discussions
- Linear Regression Models
7
Videos
- 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
4
Readings
- 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
Fitting Regression Models in R
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3
Videos
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8
Readings
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3
Discussion
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2
Assignment
2
Assignment
- Linear Regression
- End of Week Quiz
3
Discussions
- Practice with R: Why Spearman's and Pearson's may differ slightly
- Practice with R: Linear Regression
- Practice with R: Repeating the Regression Model
3
Videos
- Introduction to Week 2
- Fitting the linear regression
- Multiple Regression
8
Readings
- 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
Good Practice Multiple Regression
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4
Videos
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9
Readings
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2
Assignment
2
Assignment
- Fitting and interpreting model results
- Interpretation of interactions
4
Videos
- Introduction to Key Dataset Features: Part I
- Introduction to Key Dataset Features: Part II
- Interactions between binary variables
- Interactions between binary and continuous variables
9
Readings
- 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
Developing a Multiple Regression Model
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5
Videos
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7
Readings
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2
Discussion
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1
Plugin
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2
Assignment
2
Assignment
- Problems with automated approaches
- End of Course Quiz
2
Discussions
- Selecting an outcome; writing a research question
- What have you found?
5
Videos
- Intro to Model Development
- Variable Selection
- Developing a Model Building Strategy
- Summary of developing a Model Building Strategy
- Summary of Course
7
Readings
- 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
Auto Summary
Discover the art and science of preventing disease and promoting health with "Linear Regression in R for Public Health." This professional-level course, offered by Coursera, delves into the statistical models essential for understanding how patient and environmental factors influence health outcomes. Throughout the 900-minute course, led by expert instructors, you will learn to develop and apply linear regression models using R, a powerful and widely-used software. Starting from the basics of correlation and linear regression, you will advance to importing and examining data, fitting models, and assessing their fit and assumptions. Using respiratory disease as a case study, you will explore how various factors impact health metrics like lung function. Ideal for professionals in public health and data science, this course provides practical skills in handling and analyzing health data with R. With a starter subscription option, it offers a comprehensive introduction to building and evaluating regression models, setting the foundation for further exploration in this critical field. Join now to enhance your expertise in public health analytics and make a meaningful impact on community health.
Alex Bottle
Victoria Cornelius