Statistics for Experimenters Design, Innovation, and Discovery

ISBN-10: 0471718130

ISBN-13: 9780471718130

Edition: 2nd 2005 (Revised)

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Description: A Classic adapted to modern times Rewritten and updated, this new edition of Statistics for Experimenters adopts the same approaches as the landmark First Edition by teaching with examples, readily understood graphics, and the appropriate use of computers. Catalyzing innovation, problem solving, and discovery, the Second Edition provides experimenters with the scientific and statistical tools needed to maximize the knowledge gained from research data, illustrating how these tools may best be utilized during all stages of the investigative process. The authors’ practical approach starts with a problem that needs to be solved and then examines the appropriate statistical methods of design and analysis. Providing even greater accessibility for its users, the Second Edition is thoroughly revised and updated to reflect the changes in techniques and technologies since the publication of the classic First Edition. Among the new topics included are: Graphical Analysis of Variance Computer Analysis of Complex Designs Simplification by transformation Hands-on experimentation using Response Service Methods Further development of robust product and process design using split plot arrangements and minimization of error transmission Introduction to Process Control, Forecasting and Time Series Illustrations demonstrating how multi-response problems can be solved using the concepts of active and inert factor spaces and canonical spaces Bayesian approaches to model selection and sequential experimentation An appendix featuring Quaquaversal quotes from a variety of sources including noted statisticians and scientists to famous philosophers is provided to illustrate key concepts andenliven the learning process. All the computations in the Second Edition can be done utilizing the statistical language R. Functions for displaying ANOVA and lamba plots, Bayesian screening, and model building are all included and R packages are available online. All theses topics can also be applied utilizing easy-to-use commercial software packages. Complete with applications covering the physical, engineering, biological, and social sciences, Statistics for Experimenters is designed for individuals who must use statistical approaches to conduct an experiment, but do not necessarily have formal training in statistics. Experimenters need only a basic understanding of mathematics to master all the statistical methods presented. This text is an essential reference for all researchers and is a highly recommended course book for undergraduate and graduate students.

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Book details

List price: $168.00
Edition: 2nd
Copyright year: 2005
Publisher: John Wiley & Sons, Incorporated
Publication date: 5/31/2005
Binding: Hardcover
Pages: 664
Size: 6.50" wide x 9.50" long x 1.25" tall
Weight: 2.882

GEORGE E. P. BOX, PhD, DSc, is Ronald Aylmer Fisher Professor Emeritus of Statistics and Industrial Engineering at the University of Wisconsin-Madison. He is a Fellow of the Royal Society, an Honorary Fellow and Shewhart and Deming Medalist of the American Society for Quality and was awarded the Guy Medal in Gold of the Royal Statistical Society. He is also the recipient of the Samuel S. Wilks Memorial Medal of the American Statistical Association.J. STUART HUNTER, PhD, DSc, is Professor Emeritus of Civil Engineering at Princeton University. Dr. Hunter is a member of the National Academy of Engineering and has served as consultant to many industries and government agencies. He has been a staff member of the National Academy of Sciences, Committee on National Statistics; statistician in residence at the University of Wisconsin; and is the founding editor of Technometrics.The late WILLIAM G. HUNTER, PhD, was Professor of Statistics and Engineering at the University of Wisconsin-Madison.

Preface to the Second Edition
Catalizing the Generation of Knowledge
The Learning Process
Important Considerations
The Experimenter's Problem and Statistical Methods
A Typical Investigation
How to Use Statistical Techniques
References and Further Reading
Basics: Probability, Parameters and Statistics
Experimental Error
Distributions
Statistics and Parameters
Measures of Location and Spread
The Normal Distribution
Normal Probability Plots
Randomness and Random Variables
Covariance and Correlation as Measures of Linear Dependence
Student'st Distribution
Estimates of Parameters
Random Sampling from a Normal Population
The Chi-Square andF Distributions
The Binomial Distribution
The Poisson Distribution
Mean and Variance of Linear Combinations of Observations
References and Further Reading
Comparing Two Entities: Relevant Reference Distributions, Tests and Confidence Intervals
Relevant Reference Sets and Distributions
Randomized Paired Comparison Design: Boys' Shoes Example
Blocking and Randomization
Reprise: Comparison, Replication, Randomization, and Blocking in Simple Experiments
More on Significance Tests
Inferences About Data that are Discrete: Binomial Distribution
Inferences about Frequencies (Counts Per Unit): The Poisson Distribution
Contingency Tables and Tests of Association
Comparison of the Robustness of Tests to Compare Two Entities
Calculation of reference distribution from past data.References and Further Reading
Comparing a Number of Entities: Randomized Blocks and Latin Squares
Comparingk Treatments in a Fully Randomized Design
Randomized Block Designs
A Preliminary Note on Split-Plot Experiments and their Relationship to Randomized Blocks
More than one blocking component: Latin Squares
Balanced Incomplete Block Designs
The Rationale for the Graphical ANOVA
Some Useful Latin Square, Graeco-Latin Square, and Hyper-Graeco-Latin Square Designs
References and Further Reading
Factorial Designs at Two Levels: Advantages of Experimental Design
Introduction
Example 1: The Effects of Three Factors (Variables) on Clarity of Film
Example 2: The Effects of Three Factors on Three Physical Properties of a Polymer Solution
A 23 Factorial Design: Pilot Plant Investigation
Calculation of Main Effects
Interaction Effects
Genuine Replicate Runs
Interpretation of Results
The Table of Contrasts
Misuse of the ANOVA for 2k Factorial Experiments
Eyeing the Data
Dealing with More Than One Response: A Pet Food Experiment
A 24 Factorial Design: Process Development Study
Analysis Using Normal and Lenth Plots
Other Models for Factorial Data
Blocking the 2k Factorial Designs
Learning by Doing
Summary
Blocking Larger Factorial Designs
Partial Confounding.References and Further Reading
Fraction Factorial Designs: Economy in Experimentation
Effects of Five Factors on Six Properties of Films in Eight Runs
Stability of New Product, Four Factors in Eight Runs, a 24a??1 Design
A Half-Fraction Example: The Modification of a Bearing
The Anatomy of the Half Fraction
The 27a??4III Design: A Bicycle Example
Eight-Run Designs
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