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Design and Analysis of Experiments with R PDF

618 Pages·2014·6.04 MB·English
by  Lawson
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Design and Analysis of Experiments with R https://www.facebook.com/groups/stats.ebooksandpapers/ https://www.facebook.com/groups/stats.ebooksandpapers/ CHAPMAN & HALL/CRC Texts in Statistical Science Series Series Editors Francesca Dominici, Harvard School of Public Health, USA Julian J. Faraway, University of Bath, UK Martin Tanner, Northwestern University, USA Jim Zidek, University of British Columbia, Canada Statistical Theory: A Concise Introduction Modelling Binary Data, Second Edition F. Abramovich and Y. Ritov D. Collett Practical Multivariate Analysis, Fifth Edition Modelling Survival Data in Medical Research, A. Afifi, S. May, and V.A. Clark Third Edition D. Collett Practical Statistics for Medical Research D.G. Altman Introduction to Statistical Methods for Clinical Trials Interpreting Data: A First Course T.D. Cook and D.L. DeMets in Statistics A.J.B. Anderson Applied Statistics: Principles and Examples Introduction to Probability with R D.R. Cox and E.J. Snell K. Baclawski Multivariate Survival Analysis and Competing Linear Algebra and Matrix Analysis for Risks Statistics M. Crowder S. Banerjee and A. Roy Statistical Analysis of Reliability Data Analysis of Categorical Data with R M.J. Crowder, A.C. Kimber, C. R. Bilder and T. M. Loughin T.J. Sweeting, and R.L. Smith Statistical Methods for SPC and TQM An Introduction to Generalized D. Bissell Linear Models, Third Edition A.J. Dobson and A.G. Barnett Introduction to Probability J. K. Blitzstein and J. Hwang Nonlinear Time Series: Theory, Methods, and Applications with R Examples Bayesian Methods for Data Analysis, R. Douc, E. Moulines, and D.S. Stoffer Third Edition B.P. Carlin and T.A. Louis Introduction to Optimization Methods and Their Applications in Statistics Second Edition B.S. Everitt R. Caulcutt Extending the Linear Model with R: The Analysis of Time Series: An Introduction, Generalized Linear, Mixed Effects and Sixth Edition Nonparametric Regression Models C. Chatfield J.J. Faraway Introduction to Multivariate Analysis Linear Models with R, Second Edition C. Chatfield and A.J. Collins J.J. Faraway Problem Solving: A Statistician’s Guide, A Course in Large Sample Theory Second Edition T.S. Ferguson C. Chatfield Multivariate Statistics: A Practical Statistics for Technology: A Course in Applied Approach Statistics, Third Edition B. Flury and H. Riedwyl C. Chatfield Readings in Decision Analysis Bayesian Ideas and Data Analysis: An S. French Introduction for Scientists and Statisticians R. Christensen, W. Johnson, A. Branscum, and T.E. Hanson https://www.facebook.com/groups/stats.ebooksandpapers/ Markov Chain Monte Carlo: Design and Analysis of Experiments with SAS Stochastic Simulation for Bayesian Inference, J. Lawson Second Edition A Course in Categorical Data Analysis D. Gamerman and H.F. Lopes T. Leonard Bayesian Data Analysis, Third Edition Statistics for Accountants A. Gelman, J.B. Carlin, H.S. Stern, D.B. Dunson, S. Letchford A. Vehtari, and D.B. Rubin Introduction to the Theory of Statistical Multivariate Analysis of Variance and Inference Repeated Measures: A Practical Approach for H. Liero and S. Zwanzig Behavioural Scientists Statistical Theory, Fourth Edition D.J. Hand and C.C. Taylor B.W. Lindgren Practical Longitudinal Data Analysis Stationary Stochastic Processes: Theory and D.J. Hand and M. Crowder Applications Logistic Regression Models G. Lindgren J.M. Hilbe The BUGS Book: A Practical Introduction to Richly Parameterized Linear Models: Bayesian Analysis Additive, Time Series, and Spatial Models D. Lunn, C. Jackson, N. Best, A. Thomas, and Using Random Effects D. Spiegelhalter J.S. Hodges Introduction to General and Generalized Statistics for Epidemiology Linear Models N.P. Jewell H. Madsen and P. Thyregod Stochastic Processes: An Introduction, Time Series Analysis Second Edition H. Madsen P.W. Jones and P. Smith Pólya Urn Models The Theory of Linear Models H. Mahmoud B. Jørgensen Randomization, Bootstrap and Monte Carlo Principles of Uncertainty Methods in Biology, Third Edition J.B. Kadane B.F.J. Manly Graphics for Statistics and Data Analysis with R Introduction to Randomized Controlled K.J. Keen Clinical Trials, Second Edition Mathematical Statistics J.N.S. Matthews K. Knight Statistical Methods in Agriculture and Introduction to Multivariate Analysis: Experimental Biology, Second Edition Linear and Nonlinear Modeling R. Mead, R.N. Curnow, and A.M. Hasted S. Konishi Statistics in Engineering: A Practical Approach Nonparametric Methods in Statistics with SAS A.V. Metcalfe Applications Statistical Inference: An Integrated Approach, O. Korosteleva Second Edition Modeling and Analysis of Stochastic Systems, H. S. Migon, D. Gamerman, and Second Edition F. Louzada V.G. Kulkarni Beyond ANOVA: Basics of Applied Statistics Exercises and Solutions in Biostatistical Theory R.G. Miller, Jr. L.L. Kupper, B.H. Neelon, and S.M. O’Brien A Primer on Linear Models Exercises and Solutions in Statistical Theory J.F. Monahan L.L. Kupper, B.H. Neelon, and S.M. O’Brien Applied Stochastic Modelling, Second Edition Design and Analysis of Experiments with R B.J.T. Morgan J. Lawson Elements of Simulation B.J.T. Morgan https://www.facebook.com/groups/stats.ebooksandpapers/ Probability: Methods and Measurement Applied Nonparametric Statistical Methods, A. O’Hagan Fourth Edition P. Sprent and N.C. Smeeton Introduction to Statistical Limit Theory A.M. Polansky Data Driven Statistical Methods P. Sprent Applied Bayesian Forecasting and Time Series Analysis Generalized Linear Mixed Models: A. Pole, M. West, and J. Harrison Modern Concepts, Methods and Applications W. W. Stroup Statistics in Research and Development, Time Series: Modeling, Computation, and Survival Analysis Using S: Analysis of Inference Time-to-Event Data R. Prado and M. West M. Tableman and J.S. Kim Introduction to Statistical Process Control Applied Categorical and Count Data Analysis P. Qiu W. Tang, H. He, and X.M. Tu Sampling Methodologies with Applications Elementary Applications of Probability Theory, P.S.R.S. Rao Second Edition H.C. Tuckwell A First Course in Linear Model Theory N. Ravishanker and D.K. Dey Introduction to Statistical Inference and Its Applications with R Essential Statistics, Fourth Edition M.W. Trosset D.A.G. Rees Understanding Advanced Statistical Methods Stochastic Modeling and Mathematical P.H. Westfall and K.S.S. Henning Statistics: A Text for Statisticians and Quantitative Scientists Statistical Process Control: Theory and F.J. Samaniego Practice, Third Edition G.B. Wetherill and D.W. Brown Statistical Methods for Spatial Data Analysis O. Schabenberger and C.A. Gotway Generalized Additive Models: An Introduction with R Bayesian Networks: With Examples in R S. Wood M. Scutari and J.-B. Denis Epidemiology: Study Design and Large Sample Methods in Statistics Data Analysis, Third Edition P.K. Sen and J. da Motta Singer M. Woodward Decision Analysis: A Bayesian Approach Practical Data Analysis for Designed J.Q. Smith Experiments Analysis of Failure and Survival Data B.S. Yandell P. J. Smith Applied Statistics: Handbook of GENSTAT Analyses E.J. Snell and H. Simpson https://www.facebook.com/groups/stats.ebooksandpapers/ Texts in Statistical Science Design and Analysis of Experiments with R John Lawson Brigham Young University Provo, Utah, USA https://www.facebook.com/groups/stats.ebooksandpapers/ CRC Press Taylor & Francis Group 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 33487-2742 © 2015 by Taylor & Francis Group, LLC CRC Press is an imprint of Taylor & Francis Group, an Informa business No claim to original U.S. Government works Version Date: 20150406 International Standard Book Number-13: 978-1-4987-2848-5 (eBook - PDF) This book contains information obtained from authentic and highly regarded sources. Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. 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Visit the Taylor & Francis Web site at http://www.taylorandfrancis.com and the CRC Press Web site at http://www.crcpress.com https://www.facebook.com/groups/stats.ebooksandpapers/ Contents Preface xi List of Examples xv 1 Introduction 1 1.1 Statistics and Data Collection 1 1.2 Beginnings of Statistically Planned Experiments 2 1.3 Definitions and Preliminaries 2 1.4 Purposes of Experimental Design 5 1.5 Types of Experimental Designs 6 1.6 Planning Experiments 7 1.7 Performing the Experiments 9 1.8 Use of R Software 12 1.9 Review of Important Concepts 12 1.10 Exercises 15 2 Completely Randomized Designs with One Factor 17 2.1 Introduction 17 2.2 Replication and Randomization 17 2.3 A Historical Example 19 2.4 Linear Model for CRD 20 2.5 Verifying Assumptions of the Linear Model 29 2.6 Analysis Strategies When Assumptions Are Violated 31 2.7 Determining the Number of Replicates 38 2.8 Comparison of Treatments after the F-test 41 2.9 Review of Important Concepts 49 2.10 Exercises 51 3 Factorial Designs 55 3.1 Introduction 55 3.2 Classical One at a Time versus Factorial Plans 55 3.3 Interpreting Interactions 57 3.4 Creating a Two-Factor Factorial Plan in R 60 3.5 Analysis of a Two-Factor Factorial in R 61 3.6 Factorial Designs with Multiple Factors—CRFD 80 3.7 Two-Level Factorials 85 vii https://www.facebook.com/groups/stats.ebooksandpapers/ hhttttppss::////wwwwww..ffaacceebbooookk..ccoomm//ggrroouuppss//ssttaattss..eebbooookkssaannddppaappeerrss// viii CONTENTS 3.8 Verifying Assumptions of the Model 102 3.9 Review of Important Concepts 106 3.10 Exercises 108 4 Randomized Block Designs 113 4.1 Introduction 113 4.2 Creating an RCB in R 114 4.3 Model for RCB 116 4.4 An Example of an RCB 117 4.5 Determining the Number of Blocks 121 4.6 Factorial Designs in Blocks 122 4.7 Generalized Complete Block Design 124 4.8 Two Block Factors LSD 128 4.9 Review of Important Concepts 133 4.10 Exercises 135 4.11 Appendix—Data from the Golf Experiment 140 5 Designs to Study Variances 141 5.1 Introduction 141 5.2 Random Factors and Random Sampling Experiments 142 5.3 One-Factor Sampling Designs 144 5.4 Estimating Variance Components 145 5.5 Two-Factor Sampling Designs 155 5.6 Nested Sampling Experiments (NSE) 164 5.7 Staggered Nested Designs 167 5.8 Designs with Fixed and Random Factors 173 5.9 Graphical Methods to Check Model Assumptions 180 5.10 Review of Important Concepts 188 5.11 Exercises 190 6 Fractional Factorial Designs 193 6.1 Introduction 193 6.2 Half-Fractions of 2k Designs 194 6.3 Quarter and Higher Fractions of 2k Designs 204 6.4 Criteria for Choosing Generators for 2k−p Designs 206 6.5 Augmenting Fractional Factorials 218 6.6 Plackett-Burman (PB) and Model Robust Screening Designs 229 6.7 Mixed Level Factorials and Orthogonal Arrays (OAs) 242 6.8 Definitive Screening Designs 248 6.9 Review of Important Concepts 250 6.10 Exercises 252 7 Incomplete and Confounded Block Designs 261 7.1 Introduction 261 7.2 Balanced Incomplete Block (BIB) Designs 262 https://www.facebook.com/groups/stats.ebooksandpapers/ https://www.facebook.com/groups/stats.ebooksandpapers/ CONTENTS ix 7.3 Analysis of Incomplete Block Designs 264 7.4 BTIB and PBIB Designs 267 7.5 Row Column Designs 271 7.6 Confounded 2k and 2k−p Designs 272 7.7 Confounding 3-Level and p-Level Factorial Designs 285 7.8 Blocking Mixed Level Factorials and OAs 287 7.9 Partially Confounded Blocked Factorial (PCBF) 295 7.10 Review of Important Concepts 300 7.11 Exercises 303 8 Split-Plot Designs 307 8.1 Introduction 307 8.2 Split-Plot Experiments with CRD in Whole Plots CRSP 308 8.3 RCB in Whole Plots RBSP 315 8.4 Analysis Unreplicated 2k Split-Plot Designs 323 8.5 2k−p Fractional Factorials in Split Plots (SPFFs) 328 8.6 Sample Size and Power Issues for Split-Plot Designs 341 8.7 Review of Important Concepts 343 8.8 Exercises 345 9 Crossover and Repeated Measures Designs 351 9.1 Introduction 351 9.2 Crossover Designs (CODs) 351 9.3 Simple AB, BA Crossover Designs for Two Treatments 352 9.4 Crossover Designs for Multiple Treatments 361 9.5 Repeated Measures Designs 366 9.6 Univariate Analysis of Repeated Measures Designs 368 9.7 Review of Important Concepts 377 9.8 Exercises 379 10 Response Surface Designs 383 10.1 Introduction 383 10.2 Fundamentals of Response Surface Methodology 383 10.3 Standard Designs for Second Order Models 387 10.4 Creating Standard Response Surface Designs in R 396 10.5 Non-Standard Response Surface Designs 400 10.6 Fitting the Response Surface Model with R 407 10.7 Determining Optimum Operating Conditions 413 10.8 Blocked Response Surface (BRS) Designs 426 10.9 Response Surface Split-Plot (RSSP) Designs 430 10.10 Review of Important Concepts 439 10.11 Exercises 441 11 Mixture Experiments 447 11.1 Introduction 447 https://www.facebook.com/groups/stats.ebooksandpapers/ https://www.facebook.com/groups/stats.ebooksandpapers/

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Design and Analysis of Experiments with R presents a unified treatment of experimental designs and design concepts commonly used in practice. It connects the objectives of research to the type of experimental design required, describes the process of creating the design and collecting the data, show
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