Design and Analysis of Experiments With SAS

Design and Analysis of Experiments with SAS Provides practical guidance on the computer analysis of experimental data. It connects the objectives of research to the type of experimental design required, describes the actual process of creating the design and collecting the data, shows how to perform the proper analysis of the data, and illustrates the interpretation of results.

Drawing on a variety of application areas, from pharmaceuticals to machinery, the book presents numerous examples of experiments and exercises that enable readers to perform their own experiments. It includes examples of SAS data step programming and IML, along with procedures from SAS Stat, SAS QC, and SAS OR. The author emphasizes how the sample size, the assignment of experimental units to combinations of treatment factor levels (error control), and the selection of treatment factor combinations (treatment design) affect the resulting variance and bias of estimates as well as the validity of conclusions.

This book covers both classical ideas in experimental design and the latest research topics. It clearly discusses the objectives of a research project that lead to an appropriate design choice, the practical aspects of creating a design and performing experiments, and the interpretation of the results of computer data analysis.

Features

Emphasizes the connection between the experimental units, the way treatments are randomized to experimental units, and the proper error term for an analysis of data

Uses SAS 9.2 to illustrate the construction of experimental designs and analysis of data

Shows how to display experimental results graphically using SAS 9.2 ods graphics

Provides uniform coverage on experimental designs and design concepts that are most commonly used in practice

Presents many application from the pharmaceutical, agricultural, industrial chemicals, and machinery industries

Offers all the SAS code for examples on the author's website

John Lawson is a professor in the Department of Statistics at Brigham Young University.