Applied Biostatistics: Bivariate Data and Regression Analysis

This lecture covers bivariate data analysis, correlation, simple and multiple linear regression, confidence intervals, prediction intervals, model selection, influential points, and diagnostics for model assessment. It explains scatterplots, numerical summaries, correlation interpretation, least squares method, parameter interpretation, and regression coefficients. The lecture also discusses homoscedasticity, heteroscedasticity, matrix algebra for regression, regression estimation output interpretation, and tests/confidence intervals for coefficients. Additionally, it explores the geometry of least squares, including the Pythagorean theorem and the geometry of least squares in a visual manner.