3470:663 Experimental Design, 3 Credits Offered: Spring Prerequisite: A Course in Applied Statistical Techniques Overview: This course presents modern and classical statistical experimental design techniques along with applications in a variety of data analysis settings. Topics Include: 1. One Factor Randomized Design One Factor Model and Assumptions - Fixed and Random Effects ANOVA Procedures - Sums of Squares Partition, Expected Mean Squares Tests on Treatments - Contrasts, Multiple Comparisons Sample Size and Power Checking Model Assumptions 2. Randomized Block Designs One Factor Block - Model, ANOVA procedures, Expected Mean Squares Balanced Incomplete Block Designs Latin Square Designs 3. Two Factors Crossed Designs Models and Assumptions - Fixed Mixed and Random Effects ANOVA Procedures - Sums of Squares Partition, Expected Mean Squares Interaction Analysis - Profiles and Transformations 4. Higher Order Crossed Designs Three Factor Designs - Testing Main Effects and Interaction 2k Factorial Designs - ANOVA, Fixed, Random and Mixed Effects Pooling Techniques Fractional Factorial Designs - Confounding Partial Confounding Schemes 5. Higher Order Nested Designs Two Factor Nested Design - ANOVA, Fixed, Random and Mixed Effects Hierarchal Nested Designs - Analysis of Various Models 6. Split Plot - Repeat Measures Designs Models and Assumptions - Combinations of Main and Nested Factors ANOVA Procedures - Expected Mean Squares 7. Analysis of Covariance Suggested Text Effective Spring 2006: Design and Analysis of Experiments, Montgomery, 6th Ed., 2005, ISBN 0-471-48735-X