Analysis of Messy Data Volume 1: Designed Experiments

About this book

This practical tool for statisticians offers techniques and methods for analyzing non-standard or messy data sets that arise from experimental design situations. Topics discussed include analysis of variance techniques, such as one- and two-way analyses of variance and multiple-comparison procedures. The book introduces each topic with several examples, follows up with a more theoretical discussion, and concludes with a case study. It emphasizes the distinction between design structure and the structure of treatments, and focuses on practical implementation, including computers, with several available statistical packages, including SAS, BMD and SPSS.

Contents

The Simplest Case-One-Way Treatment Structure in a Completely Randomized Design Structure with Homogeneous Errors. One-Way Treatment Structure in a Completely Randomized Design Structure with Heterogeneous Errors. Simultaneous Inference Procedures and Multiple Comparisons. Basics of Experimental Design. Experimental Designs Involving Several Sizes of Experimental Units. Matrix Form of the Model. Balanced Two-Way Treatment Structures. Case Study: Complete Analyses of Balanced Two-Way Experiments. Using the Means Model to Analyze Balanced Two-Way Treatment Structures with Unequal Subclass Numbers. Using the Effects Model to Analyze Balanced Two-Way Treatment Structures with Unequal Subclass Numbers. Analyzing Large Balanced Two-Way Experiments with Unequal Subclass Numbers. Case Study: Balanced Two-Way Treatment Structure with Unequal Subclass Numbers. Using the Means Model to Analyze Two-Way Treatment Structures with Missing Treatment Combinations. Using the Effects Model to Anal yze Two-Way Treatment Structures with Missing Treatment Combinations. Case Study: Two-Way Treatment Structure with Missing Treatment Combinations. Analyzing Three-Way and Higher-Order Treatment Structures. Case Study: Three-Way Treatment Structure with Many Missing Treatment Combinations. Random Models and Variance Components. Methods for Estimating Variance Components. Methods for Making Inference about Variance Components. Case Study: Analysis of a Random Model. Analysis of Mixed Models. Two Case Studies of a Mixed Model. Methods for Analyzing Balanced Split-Plot Designs. Strip-Plot Experimental Designs. Analysis of Repeated Measures Designs for Which the Usual Assumptions Hold. Analysis of Repeated Measures Designs for Which the Usual Assumptions Do Not Hold. Analyzing Split-Plot and Certain Repeated Measures Experiments with Unbalanced and Missing Data. Computing the Variances of Contrasts for Repeated Measures and Split-Plot Designs by Using Hartley's Method of Synthesis. A nalysis of Nested Designs. Analysis of Repeated Measures Experiments by Using Multivariate Methods. Analysis of Crossover Designs.

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