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The approach in this introductory book is that of informal study of the data. Methods range from plotting picture-drawing techniques to rather elaborate numerical summaries. Several of the methods are the original creations of the author, and all can be carried out either with pencil or aided by hand-held calculator.

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Customer Reviews (7)

One of the most important books of the 20th century (and the 21st)
I reviewed this book when it first appeared in 1977 -- having been reading preliminary versions for about a decade before that.
At the time I offered the not-very-prescient opinion that it would quickly become a classic. It has.

The various reviews on this site are all correct. Yes there are still wonderful ideas to be mined (easy univariate transformations to symmetry, transforms to linearity to aid in curve fitting, the crucial importance of robustness, of looking at outliers and fringeliers, and the dominant role that graphics plays in forcing us to see what we never expected).
And yes, there is some material that is out-dated (e.g. using break points to estimate logs in your head).

But these are beside the point -- Scholars still study Talmud and Newton's Principia. Why? Obviously there are many reasons varying with the work and the reader.
For me a principal reason for reading (and rereading) EDA is to get a close look at how a first class mind works, with the hope that when faced with a similar problem we can then try to emulate him. To this day as I read EDA I can see Tukey's smiling face patiently explaining to me how to look at data and exhorting me not to miss subtle hints.

This was a book to be treasured when it first became available, and I see no reason for that judgment to change now, or in the future.

A must have for any data Analyst
This was a one of my recommended Text Books for my Undergraduate degree in Statistics. I never realized at that time how important a book this is. Year later I had to buy a second hand copy (I wonder what happened to mine) as the book was out of print. The fact is, now with computers we can just push the button and the machine grinds the data through the various parametric analyses, it will fit lines, whether it is appropriate to do so or not. That is where we have missed the data analysis plot. Tukey exposes us to the art of data exploration. How much we can learn from the data just visually without making (probably invalid) assumptions.If you are the sort of person you just drinks from the milk carton without sniffing it first, don't buy this book, and good luck to you. If you like me sniff, these are techniques you will need to sniff out the qualities of the data, before you drink it! Its back in Proint - Buy it - it's a bargain

A classic, still relevant
In the preface, Tukey writes, "this book ... exists to expose its readers and users to a considerable variety of techniques for looking more effectively at one's data."It succeeds remarkably well and is still relevant: after over 40 years, many of the techniques still have not been incorporated in commercial software and some of those that have (such as box-and-whisker plots and robust smoothing) are often emasculated.

This book has served me well for decades: I have used most of its techniques in my statistical consulting practice and, more recently, have used it as a foundation for courses in data analysis that range from a few hours to an entire semester.Students always appreciate the practical experience and set of tools they acquire.The more experienced ones comment on the insight: "I never fully understood what the box-and-whisker plot really did until now" is a recent example from a mid-career engineering professional.

Nevertheless, it is true that some of the material is outmoded due to its focus on manual calculation and some of the rest may be too idiosyncratic for most.What remains--which is plenty--can be studied on its own, because this book is designed for self-study: most of the chapter groups are independent of all but the introductory material, they provide detailed examples, ask many thought-provoking questions, and supply many datasets for practice.Tukey's methods speak for themselves through the gains in insight they provide, so he is content to show *how* to do them and to provide copious examples.What he does not do is supply the mathematical theory.If you like, you can read about that in Hoaglin, Mosteller, and Tukey's "Understanding Robust and Exploratory Data Analysis".

The highlights of this book, in terms of techniques, are:

*Chapters 1-4 on graphing data and on basic, useful data summaries: stem-and-leaf plots and n-letter summaries.Most statistical software now provides these.They are of primary interest as building blocks in more advanced analyses.

*"Straightening out plots:" simple, effective techniques to re-express the independent and dependent variables in a bivariate scatterplot so that the relationship becomes approximately linear.(Chapters 5 and 6.)I am not aware of any software that does this, but the techniques are so simple and elegant you can still carry them out with pencil and paper (or a spreadsheet) even with huge datasets.

*Smoothing sequences (chapters 7, 8, and 16).The "3rssh" method has largely been displaced by Lowess smooths in software, but the principles and ideas of smoothing, "roughing," and "re-roughing" are timeless.

*Analyses of two-way tables using median polish.This technique has recently been exploited in various fields, including spatial statistics, but only in the most elementary way.Think of this as a robust version of Analysis of Variance, with a focus on finding the effects, but without any of the mathematical apparatus.(Chapters 10 and 11.)One of the best tools for performing median polish is a spreadsheet.

*Advanced fitting of two-way tables: adding an interaction term; transforming the dependent variable; plotting the fits.(Chapters 12 and 13.)

*Techniques for re-expressing and analyzing counts and fractions.(Chapters 15, 17, and 18.)

Additional material covers "delineations" of scatterplots (chapters 8, 9, and 14) and assessing distributions (chapters 19 and 20).The latter suffers in retrospect from not using probability plotting methods.The former is worth learning but there does not seem to be any simple way to use modern statistical software to create these delineations.

In brief, this book requires no more mathematical prerequisite than facility with arithmetic, but after working through it, the diligent reader will come away with a body of techniques for understanding almost any kind of data set, including methods of time series analysis, regression, analysis of variance, and contingency table analysis.

Needs to be updated
I was not surprised to see that one of the issues raised by previous reviewers is whether or not this book is still relevant, given the enormous improvements in computing since this book was written. While I recognize that there are a lot of good ideas in this book, I must say that far too much of the text is devoted to techniques that are only relevant if one is doing data analysis on paper. This is certainly not to slander Tukey's own extensive knowledge of computing -- simply an admission of the fact that when EDA was written, few readers would have had access to any computing resources, let alone anything comparable to those that are available today. Today, the books suffers for this. A new version of this book, updated to take advantage of today's computing resources, would be quite valuable.

Fabulous
Wow. I had assumed this was out of print, but here it is. This is simply a remarkable book about the basics of thinking numerically. Even if you do your stats on a computer I recommend this. I use it to show Arts studentsthat quantitative analysis can be fun, powerful and simple. ... Read more

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WILEY-INTERSCIENCE PAPERBACK SERIES

The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists.

"Exploring Data Tables, Trends, and Shapes (EDTTS) was written as a companion volume to the same editors' book, Understanding Robust and Exploratory Data Analysis (UREDA). Whereas UREDA is a collection of exploratory and resistant methods of estimation and display, EDTTS goes a step further, describing multivariate and more complicated techniques . . . I feel that the authors have made a very significant contribution in the area of multivariate nonparametric methods. This book [is] a valuable source of reference to researchers in the area."
—Technometrics

"This edited volume . . . provides an important theoretical and philosophical extension to the currently popular statistical area of Exploratory Data Analysis, which seeks to reveal structure, or simple descriptions, in data . . . It is . . . an important reference volume which any statistical library should consider seriously."
—The Statistician

This newly available and affordably priced paperback version of Exploring Data Tables, Trends, and Shapes presents major advances in exploratory data analysis and robust regression methods and explains the techniques, relating them to classical methods. The book addresses the role of exploratory and robust techniques in the overall data-analytic enterprise, and it also presents new methods such as fitting by organized comparisons using the square combining table and identifying extreme cells in a sizable contingency table with probabilistic and exploratory approaches. The book features a chapter on using robust regression in less technical language than available elsewhere. Conceptual support for each technique is also provided. ... Read more

Customer Reviews (1)

An old classic, outdated, but still with remarkable relevance to current practice!
This is an old classic that has become somewhat obsolete because of modern computers.It was written in a time-period where calculations and data analysis were done by hand.There are, however, many aspects of the philosophy and approach to data analysis and statistics communicated in this text that are quite deep, and that are, unfortunately, omitted from many modern texts.

In terms of what chapters are still worthwhile, I think chapter 1 is timeless, and chapter 2 is also quite useful (although it will strike many people as alien--and I think this is a testimony to one of the ways in which statistical practice and pedagogy has gone somewhat astray).Chapters 3-6 are more obsolete.The subsequent chapters are mixed, with certain material and methods obsolete, but the general presentation and underlying ideas quite useful.

I find the way this book presents regression to make a lot more sense than the way it is presented in more modern books.It is evident from reading this book that the authors actually stop to think about what they're doing and why they are doing it.They have what modern authors would call "skepticism" and what I would simply call common sense.Modern books simply present the most commonly accepted practice along with various diagnostics, in a cookbook fashion.

In many respects, I think that this book reflects an earlier era in which things moved at a slower pace and there was more of an emphasis on long-term thinking.Unfortunately, in the modern day-and-age of computers, statisticians have become sloppier than ever before, and this is certainly reflected in textbooks on data analysis and regression.Many are sloppily written and encourage the next generation of statisticians to be equally sloppy.This book will root you in a more disciplined way of doing things.You won't find pages of difficult equations, and you'll find no computer code...but you will find thoughtful prose that will make you think, and that will teach you how to think about your data so that you can ask the right questions and choose the appropriate techniques and interpret them properly.

Who should read this book?A better question is, who shouldn't read this book?I only wish I could find a more modern equivalent.As far as I know, there isn't one--and if there is, I certainly have not found it yet. ... Read more

Product Description
This volume of eleven articles compiles important papers by Tukey that examine the intriguing problems inherent in the area of multiple comparisons and provide a useful framework for thinking about them.Each volume in the set is indexed and contains a bibliography. ... Read more

Customer Reviews (1)

I'm not biased
Although he was my great great uncle, I am not writing this review with bias.I found it to be a fascinating book, with a (relatively) new perspective on many matters ... Read more

Product Description
First of an eight-volume set, documenting Tukey's work from the 1940s to the 1980sOne of the late 20th Century's leading innovators and influences on data analysis, John W. Tukey's discoveries and methods have greatly impacted the work of statisticians throughout the world. The Collected Works of John W. Tukey begins here, with 14 chapters on time series analysis. ... Read more

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This volume contains six early mathematical works, four papers on fiducial inference, five on transformations, and 27 on a miscellany of topics in mathematical statistics. ... Read more

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A comprehensive summary of new and existing approaches to analyzing multiresponse data, Graphical Analysis of Multiresponse Data emphasizes graphical procedures. These procedures are then used, in various ways, to analyze, summarize, and present data from a specific, well-known plant breeding trial.These procedures result in overlap plots, their corresponding semigraphical tables, scatter plot matrices, profiles across environments and attributes for individual genotypes and groups of genotypes, and principal components.The interpretation of these displays, as an aid to understanding, is illustrated and discussed. Techniques for choosing expressions for the observed quantities are also emphasized.Graphical Analysis of Multiresponse Data is arranged into three parts:oWhat can usefully be doneoConsequences for the exampleoApproaches and choices in more detailThat structure enables the reader to obtain an overview of what can be found, and to then delve into various aspects more deeply if desired. Statisticians, data analysts, biometricians, plant breeders, behavioral scientists, social scientists, and engineering scientists will find Graphical Analysis of Multiresponse Data offers invaluable assistance. Its details are also of interest to scientists in private firms, government institutions, and research organizations who are concerned with the analysis and interpretation of experimental multiresponse data. ... Read more

Product Description
The analysis of variance is presented as an exploratory component of data analysis, while retaining the customary least squares fitting methods. Balanced data layouts are used to reveal key ideas and techniques for exploration. The approach emphasizes both the individual observations and the separate parts that the analysis produces. Most chapters include exercises and the appendices give selected percentage points of the Gaussian, t, F chi-squared and studentized range distributions. ... Read more

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This collection of essays brings together many of the world's most distinguished statisticians to discuss a wide array of the most important recent developments in data analysis. The book honors John W. Tukey, one of the most influential statisticians of the twentieth century, on the occasion of his eightieth birthday. Contributors, some of them Tukey's former students, use his general theoretical work and his specific contributions to Exploratory Data Analysis as the point of departure for their papers. They cover topics from "pure" data analysis, such as gaussianizing transformations and regression estimates, and from "applied" subjects, such as the best way to rank the abilities of chess players or to estimate the abundance of birds in a particular area. Tukey may be best known for coining the common computer term "bit," for binary digit, but his broader work has revolutionized the way statisticians think about and analyze sets of data. In a personal interview that opens the book, he reviews these extraordinary contributions and his life with characteristic modesty, humor, and intelligence. The book will be valuable both to researchers and students interested in current theoretical and practical data analysis and as a testament to Tukey's lasting influence.The essays are by Dhammika Amaratunga, David Andrews, David Brillinger, Christopher Field, Leo Goodman, Frank Hampel, John Hartigan, Peter Huber, Mia Hubert, Clifford Hurvich, Karen Kafadar, Colin Mallows, Stephan Morgenthaler, Frederick Mosteller, Ha Nguyen, Elvezio Ronchetti, Peter Rousseeuw, Allan Seheult, Paul Velleman, Maria-Pia Victoria- Feser, and Alessandro Villa. ... Read more

Customer Reviews (1)

honoring Tukey for contributions to data analysis
John Tukey was one of the great statisticians of the 20th Century. This book is a collection of essays written by former students and colleagues of Tukey and presented at a conference in honor of his 80th birthday in 1995. With his death just a few short weeks ago it is again a good time to read this book and reflect on his accomplishments. The book starts off with some biographical information on Tukey including introductory remarks from the editors and a short biography. This is followed by his curriculum vitae and a chronological list of his Ph.D. students. An interview of Tukey in the style found in the journal "Statistical Science" follows. This is then followed by the remarks of his wife at a dinner meeting. Many well known statisticians participated in the conference at Princeton and there are several nice pictures from the event. This is followed by a large collection of papers on topics near and dear to Tukey including robust regression, jackknife, the Princeton robustness year, outlier analysis, case studies and philosophy of statistics and data analysis.
In a New York Times obituary article for Tukey, it was mentioned that Tukey coined the terms software and bit which are now computer science terms. He was a pioneer in statistical computing and changed people's thinking about the value of exploratory data analysis in addtion to confirmatory analysis. He is famous for his work on data analysis, robust statistical methods, time series analysis and the fast Fourier transform and many other important advances. For beginners the box plot and the stem and leaf plot are now commonly taught to them in their first course. These were also clever little inventions of Tukey.

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Product Description
Provides a broad readership--both novice and advanced students and researchers--with a solid introduction to conditional thinking in a robust setting. Using a minimum of mathematics, it considers three types of parameters: location, scale and regression slopes. Recognizing their importance to performance evaluation, the book focuses on confrontations of a small number of suitably disparate shapes as well as on invariance. ... Read more

Customer Reviews (1)

robustness with an unfamiliar name
Configural polysampling is a methodology that is not only unfamiliar to the general public but also to many in the statistical community.It is as methodology that attempts to identify robust estimators by showing their performance for a variety of distributions.Morgenthaler and Tukey are the editors but this is not a collection of papers but rather a course text on the topic with several robustness specialist contributing whole chapters or parts of chapters.Morgenthaler and Tukey contribute so much to this text that they really should be characterized as the major authors with Cohen, Easton, O'Brien and playing more minor roles.Katherine Bell Krystink plays an intermediate role as in addition to contribution a full chapter she co-authored two chapters with Morgenthaler.

The idea is to create new population distributions by using only the observed values but giving the values varying weights to form distributions that provide challenges to the estimator.The word "configural" is used because the use of a different set of wieghts reconfigures the distribution of the data.The word "polysampling" indicates a resampling of the data (akin to bootstrap and jackknife) but doing the entire resampling more than once to allow for two or more weight functions and hence challenge distributions.

I recommend this book primarily to statisticians who have an interest in robust methods.As the book was published in 1991 it is a little surprising that it hasn't caught on yet.But that is the case with many very novel ideas. ... Read more

Product Description
This book contains many results and methods developed during the last twenty years that are not included in any other books.The book covers problems with multiple criteria, and in addition to discrete time problems it considers two types of continuous time problems.The book presents techniques based on the convex analysis and analyses of the geometrical properties of strategic measures and limiting distributions.The book is suitable for researchers and graduate students in the areas of electrical engineering, operations research, management, and applied probability.It assumes an undergraduate course in probability. ... Read more

Product Description
The Wiley Classics Library consists of selected books that have become recognized classics in their respective fields. With these new unabridged and inexpensiveeditions, Wiley hopes to extend the life of these important works by making themavailable to future generations of mathematicians and scientists.

Currently available in the Series:

T.W. Anderson
The Statistical Analysis of Time Series

T.S. Arthanari & Yadolah Dodge
Mathematical Programming in Statistics

Emil Artin
Geometric Algebra

Norman T. J. Bailey
The Elements of Stochastic Processes with Applications to the Natural Sciences

Robert G. Bartle
The Elements of Integration and

Lebesgue Measure
George E. P. Box & Norman R. Draper

Evolutionary Operation: A Statistical Method for Process Improvement
George E. P. Box & George C. Tiao

Bayesian Inference in Statistical Analysis
R. W. Carter

Finite Groups of Lie Type: Conjugacy Classes
and Complex Characters

R. W. Carter
Simple Groups of Lie Type

William G. Cochran & Gertrude M. Cox
Experimental Designs, Second Edition

Richard Courant
Differential and Integral Calculus, Volume I

Richard Courant
Differential and Integral Calculus, Volume II

Richard Courant & D. Hilbert
Methods of Mathematical Physics, Volume I

Richard Courant & D. Hilbert
Methods of Mathematical Physics, Volume II

D. R. Cox
Planning of Experiments

Harold S. M. Coxeter
Introduction to Geometry, Second Edition

Charles W. Curtis & Irving Reiner
Representation Theory of Finite Groups andAssociative Algebras

Charles W. Curtis & Irving Reiner
Methods of Representation Theory with Applications to Finite Groups and Orders, Volume I

Charles W. Curtis & Irving Reiner
Methods of Representation Theory with Applications to Finite Groups and Orders, Volume II

Cuthbert Daniel
Fitting Equations to Data: Computer Analysis of Multifactor Data, Second Edition

Bruno de Finetti
Theory of Probability, Volume I

Bruno de Finetti
Theory of Probability, Volume 2

W. Edwards Deming
Sample Design in Business Research

Amos de Shalit & Herman Feshbach
Theoretical Nuclear Physics, Volume 1— Nuclear Structure

Harold F. Dodge & Harry G. Romig
Sampling Inspection Tables: Single and Double Sampling

J. L. Doob
Stochastic Processes

Nelson Dunford & Jacob T. Schwartz
Linear Operators, Part One, General Theory

Nelson Dunford & Jacob T. Schwartz
Linear Operators, Part Two, Spectral Theory—Self Adjoint Operators in Hilbert Space

Nelson Dunford & Jacob T. Schwartz
Linear Operators, Part Three, Spectral Operators

Regina C. Elandt-Johnson & Norman L. Johnson
Survival Models and Data Analysis

Herman Feshbach
Theoretical Nuclear Physics: Nuclear Reactions

Joseph L. Fleiss
Design and Analysis of Clinical Experiments

Bernard Friedman
Lectures on Applications-Oriented Mathematics

Phillip Griffiths & Joseph Harris
Principles of Algebraic Geometry

Gerald J. Hahn & Samuel S. Shapiro
Statistical Models in Engineering

Marshall Hall, Jr.
Combinatorial Theory, Second Edition

Morris H. Hansen, William N. Hurwitz & William G. Madow
Sample Survey Methods and Theory, Volume I—Methods and Applications

Morris H. Hansen, William N. Hurwitz & William G. Madow
Sample Survey Methods and Theory, Volume II—Theory

Peter Henrici
Applied and Computational Complex Analysis, Volume 1—Power Series—Integration—Conformal Mapping—Location of Zeros

Peter Henrici
Applied and Computational Complex Analysis, Volume 2—Special Functions—Integral Transforms—Asymptotics—Continued Fractions

Peter Henrici
Applied and Computational Complex Analysis, Volume 3—Discrete Fourier Analysis—Cauchy Integrals—Construction of Conformal Maps—Univalent Functions

Peter Hilton & Yel-Chiang Wu
A Course in Modern Algebra

David C. Hoaglin, Frederick Mosteller & John W. Tukey
Understanding Robust and Exploratory Data Analysis

Harry Hochstadt
Integral Equations

Leslie Kish
Survey Sampling Shoshichi Kobayashi & Katsumi Nomizu Foundations of Differential Geometry, Volume I

Shoshichi Kobayashi & Katsumi Nomizu
Foundations of Differential Geometry, Volume 2

Erwin O. Kreyszig
Introductory Functional Analysis with Applications

William H. Louisell
Quantum Statistical Properties of Radiation

Rupert G. Miller Jr.
Survival Analysis

Ali Hasan Nayfeh
Introduction to Perturbation Techniques

Ali Hasan Nayfeh & Dean T. Mook
Nonlinear Oscillations

Emanuel Parzen
Modern Probability Theory & Its Applications

P. M. Prenter
Splines and Variational Methods

Howard Raiffa & Robert Schlaifer
Applied Statistical Decision Theory

Walter Rudin
Fourier Analysis on Groups

Lawrence S. Schulman
Techniques and Applications of Path Integration

Shayle R. Searle
Linear Models

I. H. Segel
Enzyme Kinetics: Behavior and Analysis of Rapid Equilibrium and Steady-State Enzyme Systems

C. L. Siegel
Topics in Complex Function Theory, Volume I—Elliptic Functions and Uniformization Theory

C. L. Siegel
Topics in Complex Function Theory, Volume II—Automorphic and Abelian Integrals

C. L. Siegel
Topics in Complex Function Theory, Volume III—Abelian Functions and Modular Functions of Several Variables

L. Spitzer
Physical Processes in the Interstellar Medium

J. J. Stoker
Differential Geometry

J. J. Stoker
Water Waves: The Mathematical Theory with Applications

J. J. Stoker
Nonlinear Vibrations in Mechanical and ElectricalSystems

Richard Zallen
The Physics of Amorphous Solids

Arnold Zellner
Introduction to Bayesian Inference in Econometrics ... Read more

Customer Reviews (1)

Outstanding treatment
This book is a classic. Even though it was written prior to the personal computer revolution, it's relevance is strong. The authors are fantastic at giving the reader a true feel for the analytical tools and approaches. This may not be a how-book for today, since many of the tools are now pre-programmed into software packages, but it is an excellent resource for developing the "intuitive" feeling of the subject. ... Read more

Product Description
These papers illustrate important featurescharacteristic of John Tukey's work, namely the desire to look beyond or beneath conventional set structures, the wish to detect and deal with anomalous behavior, and great technical ingenuity. ... Read more

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Chapters: John Tukey, Configural Frequency Analysis, Stemplot, Trimean, Data Reduction, Projection Pursuit, Median Polish. Source: Wikipedia. Pages: 35. Not illustrated. Free updates online. Purchase includes a free trial membership in the publisher's book club where you can select from more than a million books without charge. Excerpt: John Wilder Tukey (June 16, 1915 July 26, 2000) was an American statistician. Tukey was born in New Bedford, Massachusetts in 1915, and obtained a B.A. in 1936 and M.Sc. in 1937, in chemistry, from Brown University, before moving to Princeton University where he received a Ph.D. in mathematics. During World War II, Tukey worked at the Fire Control Research Office and collaborated with Samuel Wilks and William Cochran. After the war, he returned to Princeton, dividing his time between the university and AT ... Read more

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Chapters: W. Edwards Deming, Persi Diaconis, John Tukey, Walter A. Shewhart, Oscar Kempthorne, Joseph Leo Doob, Calyampudi Radhakrishna Rao, R. C. Bose, William Feller, George E. P. Box, Frederick Mosteller, Abraham Wald, Bernard Silverman, Donald Burkholder, Jack Kiefer, Harold Hotelling, Jerzy Neyman, Bradley Efron, Stephen Stigler, William Kruskal, Herbert Robbins, Mark Kac, President of the Institute of Mathematical Statistics, Samuel S. Wilks, Lucien le Cam, Stephen Fienberg, Jacob Wolfowitz, Morris H. Hansen, William Gemmell Cochran, Leonard Jimmie Savage, Louis Chen Hsiao Yun, Terry Speed, Wassily Hoeffding, Herman Chernoff, Elizabeth Scott, Ted Harris, J. Michael Steele, Theodore Wilbur Anderson. Source: Wikipedia. Pages: 142. Not illustrated. Free updates online. Purchase includes a free trial membership in the publisher's book club where you can select from more than a million books without charge. Excerpt: William Edwards Deming (October 14, 1900 December 20, 1993) was an American statistician, professor, author, lecturer, and consultant. Deming is widely credited with improving production in the United States during the Cold War, although he is perhaps best known for his work in Japan. There, from 1950 onward he taught top management how to improve design (and thus service), product quality, testing and sales (the last through global markets) through various methods, including the application of statistical methods. Deming made a significant contribution to Japan's later reputation for innovative high-quality products and its economic power. He is regarded as having had more impact upon Japanese manufacturing and business than any other individual not of Japanese heritage. Despite being considered something of a hero in Japan, he was only just beginning to win widespread recognition in the U.S. at the time of his death. Dr. Deming's teachings and philosophy can be seen through the results they produced when they w...More: http://booksllc.net/?id=70534 ... Read more