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A First Course in the Design of Experiments: A Linear Models Approach
 
 
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A First Course in the Design of Experiments: A Linear Models Approach [Hardcover]

John H. Skillings (Author), Donald Weber (Author)
4.0 out of 5 stars  See all reviews (1 customer review)

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Book Description

0849396719 978-0849396717 November 24, 1999 1
Most texts on experimental design fall into one of two distinct categories. There are theoretical works with few applications and minimal discussion on design, and there are methods books with limited or no discussion of the underlying theory. Furthermore, most of these tend to either treat the analysis of each design separately with little attempt to unify procedures, or they will integrate the analysis for the designs into one general technique.

A First Course in the Design of Experiments: A Linear Models Approach stands apart. It presents theory and methods, emphasizes both the design selection for an experiment and the analysis of data, and integrates the analysis for the various designs with the general theory for linear models.

The authors begin with a general introduction then lead students through the theoretical results, the various design models, and the analytical concepts that will enable them to analyze virtually any design. Rife with examples and exercises, the text also encourages using computers to analyze data. The authors use the SAS software package throughout the book, but also demonstrate how any regression program can be used for analysis.

With its balanced presentation of theory, methods, and applications and its highly readable style, A First Course in the Design of Experiments proves ideal as a text for a beginning graduate or upper-level undergraduate course in the design and analysis of experiments.

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Product Details

  • Hardcover: 696 pages
  • Publisher: CRC Press; 1 edition (November 24, 1999)
  • Language: English
  • ISBN-10: 0849396719
  • ISBN-13: 978-0849396717
  • Product Dimensions: 9.7 x 6.2 x 1.6 inches
  • Shipping Weight: 2.5 pounds (View shipping rates and policies)
  • Average Customer Review: 4.0 out of 5 stars  See all reviews (1 customer review)
  • Amazon Best Sellers Rank: #2,150,079 in Books (See Top 100 in Books)

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6 of 7 people found the following review helpful:
4.0 out of 5 stars A comprehensive book on Design of Experiments, July 31, 2000
A Kid's Review
This review is from: A First Course in the Design of Experiments: A Linear Models Approach (Hardcover)
I found this book very informative and well written. I started lecturing on the subject a year ago, before this book was published. I found a copy of this book in my University library and used it as a reference during the second half of my course. It contains a lot of material that cannot be found elsewhere and a wealth of examples. The first few Chapters are devoted to Regression which makes the book self-contained. There is an Appendix on Linear Algebra intended for those who have had little exposure to this important branch of Mathematics. For those proficient in SAS this book would be most useful.Perhaps the greatest asset of the book is the inclusion of topics I personally had difficulty finding elsewhere. There are other good books on the subject, notably by Montgomery (published by J.Wiley) and Kuehl (published by Duxbury) but for those interested in the Design of Experiments having this book as a reference at least would be most valuable. I gave this book 4 stars because of the appalling type of word-processing used. In this day and age I find it astonishing that some technical books appear to have been typed on a very old typewriter. Surely CRC Press aren't that poor.
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Inside This Book (learn more)
First Sentence:
Experiments are performed and observations (data) are generated in almost every discipline. Read the first page
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
pain threshold data, reparametrized model, comparison error level, factorial experiment replicated, full rank regression model, full rank linear model, independent estimable functions, daphnia data, secchi disk data, unadjusted sum, jth experimental unit, overall error level, pairwise treatment, factorial replicated, normal linear model, fixed effects case, whole plot error, experimental design setting, full rank case, subplot error, full rank model, general linear hypothesis, test controls the type, usual side conditions, simultaneous confidence intervals
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Squares Square, Corrected Total, General Linear Models Procedure Dependent Variable, Exercises Application Exercises, Theoretical Exercises, Std Error of Parameter Estimate Parameter, Critical Value of Studentized Range, Prove Theorem, Tukey Grouping Mean, Gauss-Markoff Theorem, Mean Std Dev Minimum Maximum, Stem Leaf, Find Cov, Sgn Rank, Extremes Lowest Obs Highest Obs, Least Squares Results Our, Multiply Stem
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