Lecture 2.1.2: Linear Regression Explained with Clinical Examples | Health Data Science

This lecture introduces Linear Regression using real-world clinical and healthcare data examples, specifically ICU length of stay and lactate levels. Designed for students of the Masters in Health Data Science, the session clearly explains how regression differs from correlation, how predictions are made, and why statistical models must be validated before clinical use. Key topics covered: • Correlation vs Regression (and why correlation is not causation) • Predicting ICU length of stay using clinical biomarkers • Ordinary Least Squares (OLS) method • Interpreting slope, intercept, and residuals • Understanding R² (coefficient of determination) • Confidence intervals and statistical significance • Model validation using residual plots • Common pitfalls: non-linearity, heteroscedasticity, outliers • Clinical interpretation and decision-making limits This lecture uses simple, intuitive explanations and healthcare-focused examples to make regression concepts accessible for beginners and professionals transitioning into health data analytics. 📘 Part of the Masters in Health Data Science academic curriculum. Subscribe to our channel for more Digital Health content! ❤️💬🔔 👍 Social Media: Facebook:   / universaldigitalhealth   Twitter:   / unidigihealth   LinkedIn:   / universal-digital-health   Instagram:   / universaldigitalhealth   TikTok:   / universaldigitalhealth