Coursera

Logistic Regression and Prediction for Health Data (Coursera)

Offered by University of Michigan,
Logistic Regression and Prediction for Health Data (Coursera)

This course introduces learners to the analysis of binary/dichotomous outcomes. Learners will become familiar with fundamental tests for two-group comparisons and statistical inference plus prediction more broadly using logistic regression. They will understand the connection between prevalence, risk ratios, and odds ratios. By the end of this course, learners will be able to understand how binary outcomes arise, how to use R to compare proportions between two groups, how to fit logistic regressions in R, how to make predictions using logistic regression, and how to assess the quality of these predictions.

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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.

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