Logistic Regression and Prediction for Health Data
By External Provider
About this course
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. 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.
What you will 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