然而此書以實務為導向，以實際可能碰到或必須解決的問題作為開始(簡言之，以一個問題作為開始)，如此一來，一開始就遇到的問題是：我的資料是屬於哪一種機率模型？我又如何驗證我選擇的模型？ 這是本書的最大特色：以問題為主要導向帶進問題，每一個問題都從問題的最起源而來，一個階段一個階段地引進所需的工具。
本書所用到的數學並不太難，卻很清楚的呈現統計如何解決實際問題。尤其是當需要一些較精細的分析時，必須用到許多假設，但是該如何處理假設錯誤、不確定的問題，本書有完整的說明。本書經過確實的問題讓讀者能夠更了解與熟悉統計的精神，且著重於概念與感覺的呈現，並非是一般教科書的統計工具的學習與熟練，故本書沒有習題。
本書並不適合作為一般入門參考書，研讀前須對統計的工具與觀念有一定的程度的了解，最好已有非常紮實的統計實力，例如至少有修過系上大學部的”統計學”這門課的實力。 念這本書的時候最好能夠設身處地的投入問題中，自己設法去解決這些問題，主動參與書中給的問題狀況，這樣書中的知識才能更轉化為自己的。[ 陳宏老師]
This book is the second edition of [G. E. P. Box, W. G. Hunter and J. S. Hunter, Statistics for experimenters, John Wiley & Sons, New York, 1978; MR0483116]. The first edition was very intensively cited by students and specialists and has been a premier guide and reference for the application of statistical methods, especially as applied to experimental design. Rewritten and updated, this new edition adopts the same approach as the first edition by demonstrating thoroughly worked examples, readily understood graphics, and the appropriate use of computers. In the revised edition the authors attain two main objectives: ``to make available to experimenters scientific and statistical tools that can greatly catalyze innovation, problem solving, and discovery'', and ``to illustrate how these tools may be used by and with subject matter specialists as their investigations proceed''. The book consists of fifteen chapters and appendix tables. Chapter 1 has methodological value for catalyzing the generation of knowledge; it gives an iterative problem solving scheme and a typical example of statistical investigation. In Chapter 2 the authors present basic material from probability and statistics in a compressed form. Chapter 3 is devoted to statistical comparison of two entities (treatments, processes, operators, or machines): reference distributions, tests, and confidence intervals. Comparison of more than two entities is discussed in Chapter 4 by using fully randomized designs, randomized block designs, and Latin squares. Chapters 5–8 are devoted to factorial designs and fractional factorial designs. Real data are frequently affected by multiple sources of variation; this situation in optimal experimental design is considered in Chapter 9. Chapter 10 is devoted to special computer problems in the LS-method considered with experimental design. Chapters 11 and 12 are devoted to response surface methods and their applications. Designing robust products and processes is discussed in Chapter 13. Some problems of process control, forecasting and time series analysis are considered in Chapter 14. Chapter 15 is devoted to ``evolutionary process operation''. The book includes problems at the end of each chapter; the problems are taken from real applications. Complete with applications covering the physical, engineering, biological, and social sciences, this book is designed for all individuals who must use statistical approaches to conduct a real experiment. Experimenters need only a basic understanding of mathematics to master all the statistical methods presented. This text is, undoubtedly, an essential reference for all researchers and an invaluable course book for undergraduate and graduate students.
Reviewer: Kharin, Yurij S. [form MathSciNet]