Data analysis is an interpretive activity. George Cobb's goals in Introduction to Design and Analysis of Experiments are to explain how to choose sound and suitable design structures and to engage the student in understanding the interpretive and constructive nature of data analysis and experimental design. Developed over years of classroom use, this text can be used as an introduction to statistics emphasizing experimental design. Technical prerequisites are kept to a minimum. It can also be used as an elementary graduate survey course. Cobb's approach and careful exposition allow students to build a deep understanding of statistical concepts over time as they analyze and design experiments. He presents the field of statistics to students as a matrix, rather than a hierarchy, of related concepts. This text introduces the main concepts of statistical thinking in the context of experimental design and ANOVA, allowing the material to be covered in half of the usual time. Introduction to Design and Analysis of Experiments was originally published by Springer-Verlag, New York and is now being reprinted by Key College Publishing. It is available for sale from either publisher.
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Data analysis is an interpretive activity. George Cobb's goals in Introduction to Design and Analysis of Experiments are to explain how to choose sound and suitable design structures and to engage the student misunderstanding the interpretive and constructive nature of data analysis and experimental design. Developed over years of classroom use, this text can be used as an introduction to statistics emphasizing experimental design. Technical prerequisites are kept to a minimum. It can also be used as an elementary graduate survey course.
Cobb's approach and careful exposition allow students to build a deep understanding of Statistical concepts over time as they analyze and design experiments. He presents the Statistics concepts over time as they analyze and design experiments. he presents the field of statistics to students as a matrix, rather than a hierarchy, of related concepts.
This text introduces the main concepts of Statistical thinking in the context of experimental design and ANOVA, allowing the material to be covered in half of the usual time.
Introduction to Design and Analysis of Experiments was originally published by Springer-Verlag, New York and is now being reprinted by Key College Publishing. It is available for sale from either publisher. --This text refers to an alternate Paperback edition.
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3 of 3 people found the following review helpful:
5.0 out of 5 stars
good elementary treatment of experimental design,
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This review is from: Introduction to Design and Analysis of Experiments (Key Curriculum Press) (Paperback)
Many good books on experimental designs are avaialble. Nost are written on an advanced level and others either have an agricultural or an engineering orientation. This book by Cobb is different. It is excellently written on an elementary level and it covers basic principles involved with designs before delving into specific details.
1 of 3 people found the following review helpful:
1.0 out of 5 stars
AWFUL!!,
By Dan (Washington, DC) - See all my reviews
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This review is from: Introduction to Design and Analysis of Experiments (Paperback)
What was this author thinking?? This book is not written like a traditional textbook, instead the author tries to be funny... Instead it comes across awkwardly and annoyingly. Also, the book makes it very difficult to find information in it because of the poor organization.
If you are a professor thinking about using this for your class... PLEASE DON'T!!
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Inside This Book
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Key Phrases - Statistically Improbable Phrases (SIPs):
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category condition, frame size, supplementary examples, silver blaze, leafhopper data, walking babies experiment, sleeping shrews, one piece for each factor, dentist effects, numerical carrier, individual chance errors, factor for chance error, nuisance influences, hamster data, general decomposition rule, short conc, one nuisance factor, mixed interaction terms, factorial crossing, basic decomposition step, concentrations for long days, reusing subjects, two nuisance factors, basic factorial design, balloon summary Key Phrases - Capitalized Phrases (CAPs): (learn more)
Exercise Set, Grand Avg, Cond Avg, Dietary Control, Review Exercise, Reread Example, Free Idio, Scatterplot Res, Nuisance Main, Ronald Fisher, Cond Eff, Con Suc Glu Fru, Cell Avg, Reread Exercise, Row Eff, Chance Skeptic, Col Eff, Times Lakes, Sesame Street, Chemical Treatment, Block Avg, Mount Holyoke College, Physical Grade, Grand Vitamin Residual, Method Method Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
category condition, frame size, supplementary examples, silver blaze, leafhopper data, walking babies experiment, sleeping shrews, one piece for each factor, dentist effects, numerical carrier, individual chance errors, factor for chance error, nuisance influences, hamster data, general decomposition rule, short conc, one nuisance factor, mixed interaction terms, factorial crossing, basic decomposition step, concentrations for long days, reusing subjects, two nuisance factors, basic factorial design, balloon summary Key Phrases - Capitalized Phrases (CAPs): (learn more)
Exercise Set, Grand Avg, Cond Avg, Dietary Control, Review Exercise, Reread Example, Free Idio, Scatterplot Res, Nuisance Main, Ronald Fisher, Cond Eff, Con Suc Glu Fru, Cell Avg, Reread Exercise, Row Eff, Chance Skeptic, Col Eff, Times Lakes, Sesame Street, Chemical Treatment, Block Avg, Mount Holyoke College, Physical Grade, Grand Vitamin Residual, Method Method Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
5
books
cite this book:
- Food Product Design: A Computer-Aided Statistical Approach by Ruguo Hu in Back Matter
- Experimental Design for Formulation (ASA-SIAM Series on Statistics and Applied Probability) by Wendell F. Smith in Back Matter
- Statistical Models (Cambridge Series in Statistical and Probabilistic Mathematics) by A. C. Davison in Back Matter
- Industrial Mathematics by M. C. Joshi on page 304
- Statistics for Microarrays: Design, Analysis and Inference by Ernst Wit in Back Matter
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