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
Logistic Regression and Prediction for Health Data
This course is part of Data Science for Health Research Specialization
Instructors: Philip S. Boonstra
Access provided by Alliance University
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
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
Tools you'll learn
Details to know
Shareable certificate
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Assessments
7 assignments
Taught in English
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This course is part of the Data Science for Health Research Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 3 modules in this course
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