Linear Relationship Data in Healthcare

This module provides a foundation in understanding correlation and linear relationships in healthcare data. Students will learn how to identify, measure, and interpret correlations between variables in healthcare datasets. The module covers correlation coefficients, scatter plots, and the basics of linear relationships that form the groundwork for regression analysis.

This module focuses on Ordinary Least Squares (OLS) linear regression with a single predictor variable. Students will learn how to formulate, estimate, and interpret simple linear regression models using healthcare data. The module covers regression equations, parameter estimation, model fit assessment, and practical implementation using R statistical programming.

This module expands on basic regression by introducing categorical variables through dummy coding and incorporating multiple predictor variables. Students will learn how to create and interpret dummy variables, build multiple regression models, and understand the relationships between multiple factors in healthcare contexts. The module covers variable selection, interaction effects, and interpretation of complex models.

This module focuses on assessing the validity and reliability of linear regression models through diagnostic testing. Students will learn how to evaluate model assumptions, identify violations, and apply remedial measures when necessary. The module covers tests for linearity, normality, homoscedasticity, multicollinearity, and outlier detection, ensuring students can determine when regression models are appropriate for healthcare data analysis.