Logistic Regression and Prediction 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

- Understand how binary outcomes arise and know the difference between prevalence, risk ratios, and odds ratios

- Use logistic regression to estimate and interpret the association between one or more predictors and a binary outcome

- Understand the principles for using logistic regression to make predictions and assessing the quality of those predictions

Syllabus

Simple Comparisons of Binary Outcomes

Module 1

This module introduces you to binary outcomes, including how they arise, how to calculate proportions, and how to compare proportions between two groups.

Introducing Logistic Regression

Module 2

In this module, you will be introduced to the ubiquitous logistic regression, one of the most common tools for measuring the association between one or more predictors and a binary outcome.

Assessing the Predictive Accuracy of Logistic Regression Models

Module 3

This module introduces you to tools for assessing the quality of a fitted logistic regression model.