Covers contingency tables, measures of association, stratified analysis, logistic regression, generalized linear models, Poisson regression, log-linear models, matched data, marginal homogeneity, and methods for ordinal outcomes.
Two-course sequence providing a foundation in quantitative and research methods for evidence-based public health, with emphasis on study design, statistical reasoning, data analysis, and interpretation of findings.
Second course in a graduate-level sequence emphasizing the selection of appropriate statistical methods, interpretation of findings, and communication of public health research results.
Introduces students to the analytic and research methods used to carry out the core functions of evidence-based public health.
Two-course sequence covering biostatistical reasoning and applied data analysis. Topics include descriptive statistics, probability, statistical inference, ANOVA and ANCOVA, linear and logistic regression, clinical trial design, nonparametric and categorical methods, factor and cluster analysis, and longitudinal and repeated-measures approaches.