Course Description
Multivariate analysis of data from designed experiments in public health. Topics include descriptive statistics, graphical methods, experimental design, Hotelling's T-square, multivariate analysis of variance, orthogonal contrasts, profile analysis, repeated measures, and discriminant analysis.
Athena Title
MULTIVARIATE DESIGN
Equivalent Courses
Not open to students with credit in STAT 8210 or STAT 8250 or ERSH 8350
Prerequisite
BIOS 7020 or STAT 6220
Semester Course Offered
Offered spring
Grading System
A - F (Traditional)
Course Objectives
This course is intended to introduce public health students to the multivariate analysis of data from randomized experiments. Students completing this course should be able to: 1. Distinguish between univariate and multivariate methods of analysis. 2. Describe why multivariate methods are appropriate. 3. Select the appropriate methods of multivariate analysis given the study design and objectives. 4. Describe experimental designs for multivariate data collection in public health. 5. Use statistical software for implementing multivariate analyses. 6. Interpret results of multivariate analyses. 7. Critically evaluate the results of multivariate analyses published in the public health literature.
Topical Outline
Matrix Review Multivariate Data Graphical Display Measures of Central Tendency, Dispersion and Association Multivariate Normal Distribution Partial Correlation Hypothesis Testing Experiment-wise Error One-Sample Hotelling's T-square Confidence Intervals and Ellipses Principles of Experimental Design Experimental Units Randomization Blocking Two-Group Comparison Paired Hotelling's T-square Two-Sample Hotelling's T-square Profile Analysis Model Diagnostics Multiple-Group Comparison Multivariate Analysis of Variance Orthogonal Contrasts for Treatment Comparisons Profile Analysis Model Diagnostics Multivariate Design Factorial Designs Within-Subjects Designs Repeated Measures Experiments and Growth Curves Multivariate Analysis of Covariance Discriminate Analysis Linear Discriminate Analysis Quadratic Discriminate Analysis Evaluation of the Quality of Classification Rules