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.
Logistic Regression and Prediction for Health Data
This course is part of Data Science for Health Research Specialization
Instructors: Philip S. Boonstra
Included with 
Recommended experience
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
Skills you'll gain
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There are 3 modules in this course
This module introduces you to binary outcomes, including how they arise, how to calculate proportions, and how to compare proportions between two groups.
What's included
11 videos8 readings2 assignments3 discussion prompts
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.
What's included
11 videos2 readings3 assignments
This module introduces you to tools for assessing the quality of a fitted logistic regression model.
What's included
16 videos3 readings2 assignments1 discussion prompt
Offered by
Recommended if you're interested in Data Analysis
Amazon Web Services
Google Cloud
Google Cloud
University of Michigan
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