Regression Models in Healthcare

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

• Use nonlinear regressions with quadratic and logarithmic dependent and independent variables.
• Use interactions between variables in regression models and interpret the results.
• Find potentially problematic data points in a regression model.
• Implement logistic regression models and interpret their results.
• Perform diagnostic tests for logistic regression models to determine their validity.
• Use ordinal, multinomial, and Poisson logistic regression models and interpret their results.

• DA-601: Introduction to Healthcare Data Analysis
• DA-602: Linear Relationship Data in Healthcare