Lawson Design and Analysis of Experiments with SAS

1. Auflage 2011
ISBN: 978-1-4398-8274-0
Verlag: CRC Press
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 596 Seiten

Reihe: Chapman & Hall/CRC Texts in Statistical Science

A culmination of the author’s many years of consulting and teaching, 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 students to perform their own experiments. Harnessing the capabilities of SAS 9.2, it includes examples of SAS data step programming and IML, along with procedures from SAS Stat, SAS QC, and SAS OR. The text also shows how to display experimental results graphically using SAS ODS graphics. 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 textbook 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. SAS code and ancillaries are available at http://lawson.mooo.com

Zielgruppe

Advanced undergraduate and first-year graduate students in statistics; statisticians, biostatisticians, and industrial engineers.

Autoren/Hrsg.

Lawson, John

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik

Inhaltsverzeichnis

Introduction
Statistics and Data Collection

Beginnings of Statistically Planned Experiments

Definitions and Preliminaries

Purposes of Experimental Design

Types of Experimental Designs

Planning Experiments

Performing the Experiments

Use of SAS Software

Completely Randomized Designs with One Factor

Introduction

Replication and Randomization

A Historical Example

Linear Model for Completely Randomized Design (CRD)

Verifying Assumptions of the Linear Model

Analysis Strategies When Assumptions Are Violated

Determining the Number of Replicates

Comparison of Treatments after the F-Test

Factorial Designs

Introduction

Classical One at a Time versus Factorial Plans

Interpreting Interactions

Creating a Two-Factor Factorial Plan in SAS

Analysis of a Two-Factor Factorial in SAS

Factorial Designs with Multiple Factors—Completely Randomized Factorial Design (CRFD)

Two-Level Factorials

Verifying Assumptions of the Model

Randomized Block Designs

Introduction

Creating a Randomized Complete Block (RCB) Design in SAS

Model for RCB

An Example of a RCB

Determining the Number of Blocks

Factorial Designs in Blocks

Generalized Complete Block Design

Two Block Factors Latin Square Design (LSD)

Designs to Study Variances

Introduction

Random Sampling Experiments (RSE)

One-Factor Sampling Designs

Estimating Variance Components

Two-Factor Sampling Designs—Factorial RSE
Nested SE

Staggered Nested SE

Designs with Fixed and Random Factors

Graphical Methods to Check Model Assumptions

Fractional Factorial Designs

Introduction to Completely Randomized Fractional Factorial (CRFF)

Half Fractions of 2k Designs

Quarter and Higher Fractions of 2k Designs

Criteria for Choosing Generators for 2k-p Designs

Augmenting Fractional Factorials
Plackett–Burman (PB) Screening Designs

Mixed-Level Fractional Factorials Orthogonal Array (OA)

Incomplete and Confounded Block Designs
Introduction
Balanced Incomplete Block (BIB) Designs

Analysis of Incomplete Block Designs
Partially Balanced Incomplete Block (PBIB) Designs—Balanced Treatment Incomplete Block (BTIB)
Youden Square Designs (YSD)
Confounded 2k and 2k-p Designs—Completely Confounded Blocked Factorial (CCBF) and Completely Confounded Blocked Fractional Factorial (CCBFF)
Confounding 3 Level and p Level Factorial Designs

Blocking Mixed Level Factorials and OAs

Partial CBF

Split-Plot Designs
Introduction

Split-Plot Experiments with CRD in Whole Plots (CRSP)
RCB in Whole Plots (RBSP)

Analysis Unreplicated 2k Split-Plot Designs

2k-p Fractional Factorials in Split Plots (FFSP)

Sample Size and Power Issues for Split-Plot Designs

Crossover and Repeated Measures Designs
Introduction
Crossover Designs (COD)
Simple AB, BA Crossover Designs for Two Treatments
Crossover Designs for Multiple Treatments
Repeated Measures Designs
Univariate Analysis of Repeated Measures Design

Response Surface Designs
Introduction
Fundamentals of Response Surface Methodology

Standard Designs for Second-Order Models—Completely Randomized Response Surface (CRRS) Designs

Creating Standard Designs in SAS

Non-Standard Response Surface Designs

Fitting the Response Surface Model with SAS

Determining Optimum Operating Conditions

Response Surface Designs in Blocks (BRS)

Response Surface Designs in Split-Plots (RSSP)

Mixture Experiments
Introduction
Models and Designs for Mixture Experiments
Creating Mixture Designs in SAS

Analysis of Mixture Experiment
Constrained Mixture Experiments
Blocking Mixture Experiments
Mixture Experiments with Process Variables
Mixture Experiments in Split Plot Arrangements

Robust Parameter Design Experiments
Introduction
Noise Sources of Functional Variation
Product Array Parameter Design Experiments
Analysis of Product Array Experiments
Single Array Parameter Design Experiments
Joint Modeling of Mean and Dispersion Effects

Experimental Strategies for Increasing Knowledge
Introduction
Sequential Experimentation
One-Step Screening and Optimization
Evolutionary Operation
Concluding Remarks

Bibliography

Index

A Review and Exercises appear at the end of each chapter.

Über Autor(innen)

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