Linear Regression Modeling for Health Data (Coursera)

All concepts taught in this course will be covered with multiple modalities: slide-based lectures, guided coding practice with the instructor, and independent but structured exercises.

This course is part of the Data Science for Health Research Specialization.

What you'll learn

- Become knowledgeable about the concept of statistical modeling and the basics of statistical inference

- Recognize, fit, and interpret a simple linear regression model

- Develop intuition to fit and interpret a multiple regression model

Syllabus

Principles of Statistical Modeling

Module 1

This module gives you a first look at the world of statistical modeling. It begins with a high-level overview of different philosophies on the question of 'what is a statistical model' and introduces you to the core ideas of traditional statistical inference and reasoning. At the end of the module, you will have an introductory understanding of important terms such as 'sample-to-population' (STOP) principle, sampling variation, and measures of statistical uncertainty. You will also get your first look at the ever popular t-test.

Simple Linear Regression

Module 2

This module takes you beyond t-test into linear regression. By the end of the module, you will understand how linear regression is a generalization of the t-test.

Multiple Linear Regression

Module 3

A key reason that linear regression is so powerful is that it allows to adjust for multiple predictors at the same time. In Module 3, you will learn how to fit regression models for multiple predictors. You will see how to interpret the resulting model and how to use it to answer different questions about your data.