How do we formally test whether individual predictors — or groups of predictors — contribute meaningfully to the model? This lecture focuses on hypothesis testing within the MLR framework.

Statistical Modeling in Epidemiology

Understand why regression requires matrix algebra.

Understand when and why to use correlation analysis

By the end of this lecture, you will be able to: Understand the purpose and application of ANOVA

This lecture reviews the foundational biostatistical concepts covered in HSTA/HEPI 552. These concepts are essential for understanding the advanced statistical modeling techniques we’ll explore in EPI 553.

Learn to work with real public health survey data in R

This checklist confirms that you have completed all the technical setup required for EPI 553.

This lecture provides a foundational introduction to R programming that you'll use throughout the course.

This document will verify that your R environment is properly configured for the course.

gtsummary flextable descriptive table regression output table summary table merge

Area Health Resource Files (ARF) Health Resource and Service Administration (HRSA) Crude Death Rate RUCC Spatial Autocorrelation Count Data Models

Spatially Autoregressive Models OLS Model Moran's I Spatial Error Model Spatial Lag Model Rent, AACOG, tidycensus

Deaths in San Antonio Census Tracts Moran's I Local Moran Clusters

STAN, MCMC, INLA NHIS Mortality Priors Random Intercepts Random Slopes WAIC

BRFSS + ACS average alcoholic drinks per day in past 30 days multilevel model with macro predictors cross-level interaction model

NHIS Good Health Obesity Cohort

NHIS; Cohort Mortality Outcome

Multi-Level Models brfss+acs health coverage by MSAs

Multi-Level Models BRFSS Age at Diabetes

Principal Component Analysis Health Index

Count Data Models: Poisson and Negative Binomial; Number days smoked in past 30 days (some day smokers)

Mortality~Race/Ethnicity+Education+Smoking

NHIS-LMF Mortality~Education+Race/Ethnicity