Linear Regression in R for Public Health

Imperial College London via Coursera

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Overview

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,...

Syllabus

INTRODUCTION TO LINEAR REGRESSION

Before jumping ahead to run a regression model, you need to understand a related concept: correlation. This week youll learn what it means and how to generate Pearsons and Spearmans correlation coefficients in R to assess the strength of the association between a risk factor or predictor and the patient outcome. Then youll be introduced to linear regression and the concept of model assumptions, a key idea underpinning so much of statistical analysis.

Linear Regression in R

Youll be introduced to the COPD data set that youll use throughout the course and will run basic descriptive analyses. Youll also practise running correlations in R. Next, youll see how to run a linear regression model, firstly with one and then with several predictors, and examine whether model assumptions hold.

Multiple Regression and Interaction

Now youll see how to extend the linear regression model to include binary and categorical variables as predictors and learn how to check the correlation between predictors. Then youll see how predictors can interact with each other and how to incorporate the necessary interaction terms into the model and interpret them. Different kinds of interactions exist and can be challenging to interpret, so we will take it slowly with worked examples and opportunities to practise.

MODEL BUILDING

The last part of the course looks at how to build a regression model when you have a choice of what predictors to include in it. It describes commonly used automated procedures for model building and shows you why they are so problematic. Lastly, youll have the chance to fit some models using a more defensible and robust approach.