Design of Experiments

Statistical Principles of Research Design and Analysis

Author: R. O. Kuehl

Publisher: Duxbury Press

ISBN: 9780534368340

Category: Mathematics

Page: 666

View: 4714

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Robert Kuehl's DESIGN OF EXPERIMENTS, Second Edition, prepares students to design and analyze experiments that will help them succeed in the real world. Kuehl uses a large array of real data sets from a broad spectrum of scientific and technological fields. This approach provides realistic settings for conducting actual research projects. Next, he emphasizes the importance of developing a treatment design based on a research hypothesis as an initial step, then developing an experimental or observational study design that facilitates efficient data collection. In addition to a consistent focus on research design, Kuehl offers an interpretation for each analysis.

Statistical Principles of Research Design and Analysis

Author: R. O. Kuehl

Publisher: Duxbury Resource Center

ISBN: 9780534188047

Category: Numerical analysis

Page: 686

View: 1826

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By emphasizing how research hypotheses relate to treatment design, this text provides an overall research design strategy. The author offers as realistic a setting as possible for conducting an actual research project.Examples, often based on actual research studies, describe the research venue and establish a specific problem; then the corresponding research hypothesis is identified with a treatment design that addresses it. The examples provide practical pointers relating the treatment design to the experiment design. The author includes: -- Recurrent emphasis on the following ideas: controlling variation, randomization, the "why" of replication, and the structure of the research design process-- Examples, based whenever possible on actual research studies, organized in a problem-solving fashion-- Numerous exercises also based on actual research studies and real data, whenever possible-- Consistent emphasis on data analysis of the design-- Strong computer orientation with computer outputs in SAS, SPSS, Minitab "RM", and BMDP-- A large array of real data sets from a broad spectrum of scientific and technological fields.

Statistical Principles for the Design of Experiments

Applications to Real Experiments

Author: R. Mead,S. G. Gilmour,A. Mead

Publisher: Cambridge University Press

ISBN: 0521862140

Category: Mathematics

Page: 572

View: 3697

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Focuses on the practical needs of applied statisticians and experimenters engaged in design, implementation and analysis in various disciplines.

Design of Experiments, Statistical Principles of Research Design and Analysis

Author: CTI Reviews

Publisher: Cram101 Textbook Reviews

ISBN: 1497027160

Category: Education

Page: 30

View: 830

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Facts101 is your complete guide to Design of Experiments, Statistical Principles of Research Design and Analysis. In this book, you will learn topics such as as those in your book plus much more. With key features such as key terms, people and places, Facts101 gives you all the information you need to prepare for your next exam. Our practice tests are specific to the textbook and we have designed tools to make the most of your limited study time.

Design of Experiments for Engineers and Scientists

Author: Jiju Antony

Publisher: Elsevier

ISBN: 0080994199

Category: Technology & Engineering

Page: 220

View: 2782

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The tools and techniques used in Design of Experiments (DoE) have been proven successful in meeting the challenge of continuous improvement in many manufacturing organisations over the last two decades. However research has shown that application of this powerful technique in many companies is limited due to a lack of statistical knowledge required for its effective implementation. Although many books have been written on this subject, they are mainly by statisticians, for statisticians and not appropriate for engineers. Design of Experiments for Engineers and Scientists overcomes the problem of statistics by taking a unique approach using graphical tools. The same outcomes and conclusions are reached as through using statistical methods and readers will find the concepts in this book both familiar and easy to understand. This new edition includes a chapter on the role of DoE within Six Sigma methodology and also shows through the use of simple case studies its importance in the service industry. It is essential reading for engineers and scientists from all disciplines tackling all kinds of manufacturing, product and process quality problems and will be an ideal resource for students of this topic. Written in non-statistical language, the book is an essential and accessible text for scientists and engineers who want to learn how to use DoE Explains why teaching DoE techniques in the improvement phase of Six Sigma is an important part of problem solving methodology New edition includes a full chapter on DoE for services as well as case studies illustrating its wider application in the service industry

Design of Experiments

An Introduction Based on Linear Models

Author: Max Morris

Publisher: CRC Press

ISBN: 1439894906

Category: Mathematics

Page: 376

View: 5067

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Offering deep insight into the connections between design choice and the resulting statistical analysis, Design of Experiments: An Introduction Based on Linear Models explores how experiments are designed using the language of linear statistical models. The book presents an organized framework for understanding the statistical aspects of experimental design as a whole within the structure provided by general linear models, rather than as a collection of seemingly unrelated solutions to unique problems. The core material can be found in the first thirteen chapters. These chapters cover a review of linear statistical models, completely randomized designs, randomized complete blocks designs, Latin squares, analysis of data from orthogonally blocked designs, balanced incomplete block designs, random block effects, split-plot designs, and two-level factorial experiments. The remainder of the text discusses factorial group screening experiments, regression model design, and an introduction to optimal design. To emphasize the practical value of design, most chapters contain a short example of a real-world experiment. Details of the calculations performed using R, along with an overview of the R commands, are provided in an appendix. This text enables students to fully appreciate the fundamental concepts and techniques of experimental design as well as the real-world value of design. It gives them a profound understanding of how design selection affects the information obtained in an experiment.

Design and Analysis of Experiments

Author: Angela Dean,Daniel Voss,Danel Draguljic

Publisher: Springer

ISBN: 3319522507

Category: Mathematics

Page: 842

View: 3161

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This book offers a step-by-step guide to the experimental planning process and the ensuing analysis of normally distributed data, emphasizing the practical considerations governing the design of an experiment. Data sets are taken from real experiments and sample SAS programs are included with each chapter. Experimental design is an essential part of investigation and discovery in science; this book will serve as a modern and comprehensive reference to the subject.

Data Analysis for Experimental Design

Author: Richard Gonzalez

Publisher: Guilford Press

ISBN: 1606230174

Category: Psychology

Page: 439

View: 3448

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This engaging text shows how statistics and methods work together, demonstrating a variety of techniques for evaluating statistical results against the specifics of the methodological design. Richard Gonzalez elucidates the fundamental concepts involved in analysis of variance (ANOVA), focusing on single degree-of-freedom tests, or comparisons, wherever possible. Potential threats to making a causal inference from an experimental design are highlighted. With an emphasis on basic between-subjects and within-subjects designs, Gonzalez resists presenting the countless "exceptions to the rule" that make many statistics textbooks so unwieldy and confusing for students and beginning researchers. Ideal for graduate courses in experimental design or data analysis, the text may also be used by advanced undergraduates preparing to do senior theses. Useful pedagogical features include: Discussions of the assumptions that underlie each statistical test Sequential, step-by-step presentations of statistical procedures End-of-chapter questions and exercises Accessible writing style with scenarios and examples This book is intended for graduate students in psychology and education, practicing researchers seeking a readable refresher on analysis of experimental designs, and advanced undergraduates preparing senior theses. It serves as a text for graduate level experimental design, data analysis, and experimental methods courses taught in departments of psychology and education. It is also useful as a supplemental text for advanced undergraduate honors courses.

Statistical Design

Author: George Casella

Publisher: Springer Science & Business Media

ISBN: 0387759646

Category: Mathematics

Page: 307

View: 2588

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Although statistical design is one of the oldest branches of statistics, its importance is ever increasing, especially in the face of the data flood that often faces statisticians. It is important to recognize the appropriate design, and to understand how to effectively implement it, being aware that the default settings from a computer package can easily provide an incorrect analysis. The goal of this book is to describe the principles that drive good design, paying attention to both the theoretical background and the problems arising from real experimental situations. Designs are motivated through actual experiments, ranging from the timeless agricultural randomized complete block, to microarray experiments, which naturally lead to split plot designs and balanced incomplete blocks. George Casella is Distinguished Professor in the Department of Statistics at the University of Florida. He is active in many aspects of statistics, having contributed to theoretical statistics in the areas of decision theory and statistical confidence, to environmental statistics, and has more recently concentrated efforts in statistical genomics. He also maintains active research interests in the theory and application of Monte Carlo and other computationally intensive methods. He is listed as an ISI "Highly Cited Researcher." In other capacities, Professor Casella has served as Theory and Methods Editor of the Journal of the American Statistical Association, 1996-1999, Executive Editor of Statistical Science, 2001-2004, and Co-Editor of the Journal of the Royal Statistical Society, Series B, 2009-2012. He has served on the Board of Mathematical Sciences of the National Research Council, 1999-2003, and many committees of both the American Statistical Association and the Institute of Mathematical Statistics. Professor Casella has co-authored five textbooks: Variance Components, 1992; Theory of Point Estimation, Second Edition, 1998; Monte Carlo Statistical Methods, Second Edition, 2004; Statistical Inference, Second Edition, 2001, and Statistical Genomics of Complex Traits, 2007.

Experimental Design and Data Analysis for Biologists

Author: Gerry P. Quinn,Michael J. Keough

Publisher: Cambridge University Press

ISBN: 1139432893

Category: Nature

Page: N.A

View: 6385

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An essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data. The text begins with a revision of estimation and hypothesis testing methods, covering both classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models. Topics covered include linear and logistic regression, simple and complex ANOVA models (for factorial, nested, block, split-plot and repeated measures and covariance designs), and log-linear models. Multivariate techniques, including classification and ordination, are then introduced. Special emphasis is placed on checking assumptions, exploratory data analysis and presentation of results. The main analyses are illustrated with many examples from published papers and there is an extensive reference list to both the statistical and biological literature. The book is supported by a website that provides all data sets, questions for each chapter and links to software.

Statistical Design and Analysis of Clinical Trials

Principles and Methods

Author: Weichung Joe Shih,Joseph Aisner

Publisher: CRC Press

ISBN: 1482250500

Category: Mathematics

Page: 244

View: 7855

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Statistical Design and Analysis of Clinical Trials: Principles and Methods concentrates on the biostatistics component of clinical trials. Developed from the authors’ courses taught to public health and medical students, residents, and fellows during the past 15 years, the text shows how biostatistics in clinical trials is an integration of many fundamental scientific principles and statistical methods. Teach Your Students How to Design, Monitor, and Analyze Clinical Trials The book begins with ethical and safety principles, core trial design concepts, the principles and methods of sample size and power calculation, and analysis of covariance and stratified analysis. It then focuses on sequential designs and methods for two-stage Phase II cancer trials to Phase III group sequential trials, covering monitoring safety, futility, and efficacy. The authors also discuss the development of sample size reestimation and adaptive group sequential procedures, explain the concept of different missing data processes, and describe how to analyze incomplete data by proper multiple imputations. Turn Your Students into Better Clinical Trial Investigators This text reflects the academic research, commercial development, and public health aspects of clinical trials. It gives students a multidisciplinary understanding of the concepts and techniques involved in designing and analyzing various types of trials. The book’s balanced set of homework assignments and in-class exercises are appropriate for students in (bio)statistics, epidemiology, medicine, pharmacy, and public health.

Design and Analysis of Ecological Experiments

Author: Samuel M. Scheiner,Jessica Gurevitch

Publisher: Oxford University Press

ISBN: 9780198030225

Category: Science

Page: 432

View: 5624

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Ecological research and the way that ecologists use statistics continues to change rapidly. This second edition of the best-selling Design and Analysis of Ecological Experiments leads these trends with an update of this now-standard reference book, with a discussion of the latest developments in experimental ecology and statistical practice. The goal of this volume is to encourage the correct use of some of the more well known statistical techniques and to make some of the less well known but potentially very useful techniques available. Chapters from the first edition have been substantially revised and new chapters have been added. Readers are introduced to statistical techniques that may be unfamiliar to many ecologists, including power analysis, logistic regression, randomization tests and empirical Bayesian analysis. In addition, a strong foundation is laid in more established statistical techniques in ecology including exploratory data analysis, spatial statistics, path analysis and meta-analysis. Each technique is presented in the context of resolving an ecological issue. Anyone from graduate students to established research ecologists will find a great deal of new practical and useful information in this current edition.

The Principles of Experimental Research

Author: K. Srinagesh

Publisher: Butterworth-Heinemann

ISBN: 9780750679268

Category: Science

Page: 410

View: 1608

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1 Experimental Research in Science: Its Name and Nature -- 2 The Importance of Definitions -- 3 Aspects of Quantification -- 4 The Purpose and Principles Involved in Experimenting -- Part II: Planning the Experiments -- 5 Defining the Problem for Experimental Research -- 6 Stating the Problem as a Hypothesis -- 7 Designing Experiments to Suit Problems -- 8 Dealing with Factors -- 9 Factors at More Than Two Levels -- Part III: The Craft Part of Experimental Research -- 10 Searching through Published Literature -- 11 Building the Experimental Setup -- Part IV: The Art of Reasoning in Scientific Research -- 12 Logic and Scientific Research -- 13 Inferential Logic for Experimental Research -- 14 Use of Symbolic Logic -- Part V: Probability and Statistics for Experimental Research -- 15 Introduction to Probability and Statistics -- 16 Randomization, Replication, and Sampling -- 17 Further Significance of Samples -- 18 Planning the Experiments in Statistical Terms -- 19 Statistical Inference ...

Design and Analysis of Experiments

Author: Douglas C. Montgomery

Publisher: John Wiley & Sons

ISBN: 1119113474

Category: Experimental design

Page: 630

View: 3307

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TRY (FREE for 14 days), OR RENT this title: www.wileystudentchoice.com Design and Analysis of Experiments, 9th Edition continues to help senior and graduate students in engineering, business, and statistics-as well as working practitioners-to design and analyze experiments for improving the quality, efficiency and performance of working systems. This bestselling text maintains its comprehensive coverage by including: new examples, exercises, and problems (including in the areas of biochemistry and biotechnology); new topics and problems in the area of response surface; new topics in nested and split-plot design; and the residual maximum likelihood method is now emphasized throughout the book.

The Design of Experiments in Neuroscience

Author: Mary Harrington

Publisher: SAGE

ISBN: 1412974321

Category: Medical

Page: 261

View: 4256

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“You are about to start on a great adventure. You are going to transition from reading about science to becoming a scientist.” -From the Preface Using engaging, disarming prose, author Mary Harrington shows neuroscience students how to go about selecting a topic, designing an experiment, analyzing the results, and publishing a paper. This text effectively illustrates basic research methods and design principles by uniquely using relevant examples from neuroscience such as the principles of design of fMRI studies, the use of transgenic mice, and conditional gene knockouts. The author also addresses basic professional ethics, fundamental statistics and data analysis tools, the range of possible experimental designs (from simple descriptive studies to multifactorial designs), and ways to control unwanted variables and avoid common pitfalls. This text is intended as either a core or supplemental text for both undergraduates and graduate students studying research methods in Neuroscience, Neuroanatomy, Neurophysiology, Neurochemistry, or Biological Psychology.

Statistical Methods in Biology

Design and Analysis of Experiments and Regression

Author: S.J. Welham,S.A. Gezan,S.J. Clark,A. Mead

Publisher: CRC Press

ISBN: 1439808783

Category: Mathematics

Page: 608

View: 3122

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Written in simple language with relevant examples, Statistical Methods in Biology: Design and Analysis of Experiments and Regression is a practical and illustrative guide to the design of experiments and data analysis in the biological and agricultural sciences. The book presents statistical ideas in the context of biological and agricultural sciences to which they are being applied, drawing on relevant examples from the authors’ experience. Taking a practical and intuitive approach, the book only uses mathematical formulae to formalize the methods where necessary and appropriate. The text features extended discussions of examples that include real data sets arising from research. The authors analyze data in detail to illustrate the use of basic formulae for simple examples while using the GenStat® statistical package for more complex examples. Each chapter offers instructions on how to obtain the example analyses in GenStat and R. By the time you reach the end of the book (and online material) you will have gained: A clear appreciation of the importance of a statistical approach to the design of your experiments, A sound understanding of the statistical methods used to analyse data obtained from designed experiments and of the regression approaches used to construct simple models to describe the observed response as a function of explanatory variables, Sufficient knowledge of how to use one or more statistical packages to analyse data using the approaches described, and most importantly, An appreciation of how to interpret the results of these statistical analyses in the context of the biological or agricultural science within which you are working. The book concludes with a guide to practical design and data analysis. It gives you the understanding to better interact with consultant statisticians and to identify statistical approaches to add value to your scientific research.

Fundamental Statistical Principles for the Neurobiologist

A Survival Guide

Author: Stephen W. Scheff

Publisher: Academic Press

ISBN: 0128050519

Category: Science

Page: 234

View: 9979

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Fundamental Statistical Principles for Neurobiologists introduces readers to basic experimental design and statistical thinking in a comprehensive, relevant manner. This book is an introductory statistics book that covers fundamental principles written by a neuroscientist who understands the plight of the neuroscience graduate student and the senior investigator. It summarizes the fundamental concepts associated with statistical analysis that are useful for the neuroscientist, and provides understanding of a particular test in language that is more understandable to this specific audience, with the overall purpose of explaining which statistical technique should be used in which situation. Different types of data are discussed such as how to formulate a research hypothesis, the primary types of statistical errors and statistical power, followed by how to actually graph data and what kinds of mistakes to avoid. Chapters discuss variance, standard deviation, standard error, mean, confidence intervals, correlation, regression, parametric vs. nonparametric statistical tests, ANOVA, and post hoc analyses. Finally, there is a discussion on how to deal with data points that appear to be "outliers" and what to do when there is missing data, an issue that has not sufficiently been covered in literature. An introductory guide to statistics aimed specifically at the neuroscience audience Contains numerous examples with actual data that is used in the analysis Gives the investigators a starting pointing for evaluating data in easy-to-understand language Explains in detail many different statistical tests commonly used by neuroscientists

Encyclopedia of Research Design

Author: Neil J. Salkind

Publisher: SAGE

ISBN: 1412961270

Category: Philosophy

Page: 1776

View: 1321

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"Comprising more than 500 entries, the Encyclopedia of Research Design explains how to make decisions about research design, undertake research projects in an ethical manner, interpret and draw valid inferences from data, and evaluate experiment design strategies and results. Two additional features carry this encyclopedia far above other works in the field: bibliographic entries devoted to significant articles in the history of research design and reviews of contemporary tools, such as software and statistical procedures, used to analyze results. It covers the spectrum of research design strategies, from material presented in introductory classes to topics necessary in graduate research; it addresses cross- and multidisciplinary research needs, with many examples drawn from the social and behavioral sciences, neurosciences, and biomedical and life sciences; it provides summaries of advantages and disadvantages of often-used strategies; and it uses hundreds of sample tables, figures, and equations based on real-life cases."--Publisher's description.

Optimal Design of Experiments

A Case Study Approach

Author: Peter Goos,Bradley Jones

Publisher: John Wiley & Sons

ISBN: 1119976162

Category: Science

Page: 304

View: 2343

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"This is an engaging and informative book on the modern practice of experimental design. The authors' writing style is entertaining, the consulting dialogs are extremely enjoyable, and the technical material is presented brilliantly but not overwhelmingly. The book is a joy to read. Everyone who practices or teaches DOE should read this book." - Douglas C. Montgomery, Regents Professor, Department of Industrial Engineering, Arizona State University "It's been said: 'Design for the experiment, don't experiment for the design.' This book ably demonstrates this notion by showing how tailor-made, optimal designs can be effectively employed to meet a client's actual needs. It should be required reading for anyone interested in using the design of experiments in industrial settings." —Christopher J. Nachtsheim, Frank A Donaldson Chair in Operations Management, Carlson School of Management, University of Minnesota This book demonstrates the utility of the computer-aided optimal design approach using real industrial examples. These examples address questions such as the following: How can I do screening inexpensively if I have dozens of factors to investigate? What can I do if I have day-to-day variability and I can only perform 3 runs a day? How can I do RSM cost effectively if I have categorical factors? How can I design and analyze experiments when there is a factor that can only be changed a few times over the study? How can I include both ingredients in a mixture and processing factors in the same study? How can I design an experiment if there are many factor combinations that are impossible to run? How can I make sure that a time trend due to warming up of equipment does not affect the conclusions from a study? How can I take into account batch information in when designing experiments involving multiple batches? How can I add runs to a botched experiment to resolve ambiguities? While answering these questions the book also shows how to evaluate and compare designs. This allows researchers to make sensible trade-offs between the cost of experimentation and the amount of information they obtain.