Applied Biostatistics: Bivariate Data Analysis

This lecture covers bivariate data analysis in applied biostatistics, focusing on correlation, simple linear regression, multiple linear regression, confidence intervals, prediction intervals, model selection, influential points, and diagnostics for model assessment. Topics include scatterplots, numerical summaries, correlation interpretation, causation, outliers, parallel lines, different lines, least squares method, parameter interpretation, regression line visualization, homoscedasticity, heteroscedasticity, regression coefficients interpretation, OLS properties, regression estimation output, tests and confidence intervals for coefficients, prediction intervals, least squares geometry, and geometrical interpretation of least squares.