Free Downloads Statistics For Experimenters: An Introduction To Design, Data Analysis, And Model Building
Introduces the philosophy of experimentation and the part that statistics play in experimentation. Emphasizes the need to develop a capability for ``statistical thinking'' by using examples drawn from actual case studies.
Hardcover: 653 pages
Publisher: John Wiley & Sons (1978)
Language: English
ISBN-10: 0471093157
ISBN-13: 978-0471093152
Product Dimensions: 6.4 x 1.4 x 9.5 inches
Shipping Weight: 2 pounds
Average Customer Review: 4.8 out of 5 stars See all reviews (25 customer reviews)
Best Sellers Rank: #158,627 in Books (See Top 100 in Books) #79 in Books > Crafts, Hobbies & Home > Crafts & Hobbies > Toys & Models > Models #111 in Books > Science & Math > Experiments, Instruments & Measurement > Methodology & Statistics #412 in Books > Textbooks > Science & Mathematics > Mathematics > Statistics
All of the reviews on this book are generally consistent in their praise for the book and the authors. I do not have any points to add to the discussion other than this:It is a credit to this version of Statistics for Experimenters that it has remained relevant throughout the years as a classic introductory text that has kept selling consistently since it was released in the 1970's. Nevertheless, unless you have a particular reason for purchasing this version, you should purchase the updated version(also available through ).The full title of the newer edition is:Statistics for Experimenters: Design, Innovation, and Discovery, 2nd EditionThe 2nd edition, written in the same engaging and readable style as the 1st, contains virtually all of the content of the 1st edition plus advances in design of experiments that have happened since the 1st edition was published.
This is an excellently written book with clear examples of how to apply statistics to everyday experimental settings. Box delves deep enough into the underlying theory to give an engineer such as myself an appreciation for the "reality" of the mathematics, but sticks to concrete examples and putting theory into practice. Each chapter follows the previous one, but each is also reasonably self-contained. Terminology is easily clarified with a quick use of the comprehensive index.Additionally, don't let the print date fool you... the book is timely.
This book was published in 1978 but as other reviewers have noted its practical methods and advice are timeless. George Box and Stu Hunter are both very famous statisticians who are also great teachers and lecturers. Bill Hunter is now deceased. All three authors have made major contributions to the design of experiments. The book is written for practitioners and in the simplest language possible. Emphasis is placed on practical designs and not optimal designs because optimal designs are very sensitive to model specification.It does not include the robust designs of Taguchi which came later and could easily be included if the authors choose to revise it.
More than twenty years after its publication, this seminal work is still the undisputable "Bible" for users of statistical experimental design. The practical insights sprinkled throughout this book are invaluable especially to non-mathematical statisticians. This book will never be out-of-date!
Not being a statistician but needing to understand Design of Experiments, I found this book to be extremely valuable. Box walks you thru the basic concepts through prediction and understanding of complex response surfaces. After taking it out of the library twice, I decided it was important to get my own copy. I now recommend it in every class I teach on optimization using finite element methods. DoE adds a whole new dimension to product performance simulation.
I used the Montgomery DOE book as an undergrad...but chatting with a Stat prof freind of mine..she recommened Box Hunter & Hunter over this. I had never covered the entire book..& was reading up on Factorial designs...I went ahead and bought Box Hunter & Hunter...(do wait & buy the 2nd edition due out in May-I think trys to sell you the old inventory if you are not careful)...nonetheless, the old edition I bought actually is much more intuitive and easy to follow that the "Design and Analysis of Experiments" book by Montgomery....I think its b/c the latter is written by an engineer..no offense to you out there...just that engineers cover so much material that there texts seem more "cookbook" like..here's how...w/ no too much intuition as to why ...probably catering to the engineer who has not the time to care about the why...I am thoutoughly enjoying the read...some of the quotes in hte book are pretty funny yet all the while relevant...
A solid excellent DOE book, however due to it's age, it obviously does not cover more recent topics, such as mixture experiments. I've run into a few chemical engineers that have read only this book and have no idea what mixture experiments are, and why they are important in their DOE work. Also, I do not remember seeing any material on split-plot designs, and this topic is very important in industrial experimentation since most experiments are split-plots whether you know it or not, and you cannot evaluate them as normal. This is no fault of the book due to its publish date, but a newer book, such as Montgomery's or Hamada & Wu should also be read through to learn about the more recent advancements in DOE.
This text has become a classic text on the subject of the statistical design of experiments. You will find this text footnoted, bibliographied, or listed as recommended reading in most of the books published on the subject today. The authors' approach to evaluating results by direct observation using graphs and plots of results helps the user to visualize the results of experiments. This is in contrast with others whose sole tool seems to be use of ANOVA methods alone. The authors also present helpful methods of data transformation and easy calculation of results. They also provide insight into correct design of an experimental model, and an easy method of sequential model building so that the user can build a larger model depending upon the results of his/her first effort. This book is an excellent value!
Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building Statistics for People Who (Think They) Hate Statistics (Salkind, Statistics for People Who(Think They Hate Statistics(Without CD)) Data Analytics: Practical Data Analysis and Statistical Guide to Transform and Evolve Any Business Leveraging the Power of Data Analytics, Data Science, ... (Hacking Freedom and Data Driven Book 2) Data Architecture: A Primer for the Data Scientist: Big Data, Data Warehouse and Data Vault Building N Gauge Model Railway Layouts (Building Model Railway Layouts Book 1) Statistics for Ecologists Using R and Excel: Data Collection, Exploration, Analysis and Presentation (Data in the Wild) The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences Big Data For Beginners: Understanding SMART Big Data, Data Mining & Data Analytics For improved Business Performance, Life Decisions & More! Data Analysis & Statistics (Mathematical Analysis for Scientists & Engineers Book 5) Using the Yosoo GM328: a guide for radio and electronics experimenters Time Series Modeling for Analysis and Control: Advanced Autopilot and Monitoring Systems (SpringerBriefs in Statistics / JSS Research Series in Statistics) Microsoft Excel 2013 Building Data Models with PowerPivot: Building Data Models with PowerPivot (Business Skills) Designing & Building Multi-Deck Model Railroads (Model Railroader) Building a Ready-To-Run Model Railroad (Model Railroader's How-To Guides) Discovering Knowledge in Data: An Introduction to Data Mining (Wiley Series on Methods and Applications in Data Mining) Data Just Right: Introduction to Large-Scale Data & Analytics (Addison-Wesley Data and Analytics) An Introduction to Statistics with Python: With Applications in the Life Sciences (Statistics and Computing) Microsoft Excel 2013 Data Analysis and Business Modeling: Data Analysis and Business Modeling (Introducing) Introduction to Model Railroading (Model Railroader's How-To Guides) Statistics and Data Analysis for Social Science
