edX
edXRegression Models in Healthcare
40 studentsINTERMEDIATEEnglish
Korshub's take
Healthcare data tells powerful stories—but only if you can read it. This intermediate course teaches advanced statistical methods designed specifically for healthcare applications: regression analysis, logistic modeling, and techniques for handling non-linear relationships and confounding variables.
Each concept is grounded in real healthcare examples, emphasizing practical interpretation and data-informed decision-making. You'll learn to evaluate assumptions, spot outliers, and distinguish signal from noise—essential skills for analysts and researchers making recommendations that affect clinical and business outcomes.
About this course
In this course, you will begin learning about more advanced multivariate statistical methods that are regularly used in healthcare data analysis. You will also practice applying these statistical methods to examples from the healthcare industry. The topics covered in this course will prepare you for interpreting data and making data-informed decisions in real-world healthcare settings. While the course focuses on application and the use of these statistical methods, there is some discussion of the mathematical underpinning, relevant formulae, and assumptions necessary for understanding the application of statistical methods.
This self-paced course is comprised of written content, video content, step-by-step follow-along activities, and assessments to reinforce your learning (Assessments available to Verified Track learners only).
The course is comprised of 4 modules that you should complete in order, as each subsequent module builds on the previous one.
- Module 1: Non-Linear Trends
- Module 2: Interacting Variables and Finding Outliers
- Module 3: Logistic Regression
- Module 4: Logistic Regression Variants
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