Course Description
Regression and analysis of variance (ANOVA) for biomedical and public health data. Simple and multiple regression models are considered, including data transformation, weighted regression, diagnostics, and model selection. Contrasts, multiple comparison, missing, and unbalanced data are considered for experimental designs including one-way and multi-way ANOVA, randomized block, and Latin squares.
Athena Title
REGRESSION & ANOVA
Semester Course Offered
Offered fall
Grading System
A - F (Traditional)
Course Objectives
Students will be able to: (1) Perform single and multiple regression analyses of complex data sets, (2) Build statistical models for a range of biostatistical applications, (3) Design biomedical experiments, (4) Perform and understand fixed-effects ANOVA; (5) Describe the statistical properties of parameter estimators for linear models.
Topical Outline
1. The linear regression model 2. Regression estimation and distribution theory 3. Single factor ANOVA with fixed and random effects 4. Randomized block and Latin square designs 5. Multifactor ANOVA 6. Random effects and mixed effects models 7. Factorial designs and multifactor interactions 8. Introduction to generalized linear models and categorical response data
Syllabus