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E-grāmata: Design and Analysis of Experiments with R

3.46/5 (24 ratings by Goodreads)
(Brigham Young University, Provo, Utah, USA)
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Writing for first-year or second-year graduate students or advanced undergraduates who intend to work in a field where they will design experiments, Lawson explains how to connect the objectives of research to the type of experimental design required, how to perform the proper analysis of the data, and how to interpret results. He minimizes the exposition on the mechanics of computation by relying on the open-source statistical software package R. Among his topics are factorial designs, designs to study variances, split-plot designs, mixture experiments, and robust parameter design experiments. Annotation ©2015 Ringgold, Inc., Portland, OR (protoview.com)

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, shows how to perform the proper analysis of the data, and illustrates the interpretation of results.

Drawing on his many years of working in the pharmaceutical, agricultural, industrial chemicals, and machinery industries, the author teaches students how to:

  • Make an appropriate design choice based on the objectives of a research project
  • Create a design and perform an experiment
  • Interpret the results of computer data analysis

The book emphasizes the connection among the experimental units, the way treatments are randomized to experimental units, and the proper error term for data analysis. R code is used to create and analyze all the example experiments. The code examples from the text are available for download on the author’s website, enabling students to duplicate all the designs and data analysis.

Intended for a one-semester or two-quarter course on experimental design, this text covers classical ideas in experimental design as well as the latest research topics. It gives students practical guidance on using R to analyze experimental data.

Recenzijas

"This is an excellent but demanding text. This book should be mandatory reading for anyone teaching a course in the statistical design of experiments. reading this text is likely to influence their course for the better." MAA Reviews, March 2015

"Thank you for writing your phenomenal book "Design and Analysis of Experiments with R". I'm teaching a new course this spring on experimental design and reinforcement learning. The students are graduate bioengineers, so I was having difficulty finding a text that blends theory, practice, and computation. Your book excels at all three. The first chapter I read clarified several topics and improved both my teaching and research. After testing a dozen DOE and RSM books, yours is the clear winner. I understand the enormous time that goes into a well-constructed textbook. I hope this message conveys my deep appreciation for your effort." Paul Jensen, Ph.D., Assistant Professor , Department of Bioengineering and Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign "This is an excellent but demanding text. This book should be mandatory reading for anyone teaching a course in the statistical design of experiments. reading this text is likely to influence their course for the better." MAA Reviews, March 2015

"In my opinion, this is a very valuable book. It covers the topics that I judge should be in such a book including what might be called the standard designs and more it has become my go to text on experimental design."

David E. Booth, Technometrics

Preface xi
List of Examples
xv
1 Introduction
1(16)
1.1 Statistics and Data Collection
1(1)
1.2 Beginnings of Statistically Planned Experiments
2(1)
1.3 Definitions and Preliminaries
2(3)
1.4 Purposes of Experimental Design
5(1)
1.5 Types of Experimental Designs
6(1)
1.6 Planning Experiments
7(2)
1.7 Performing the Experiments
9(3)
1.8 Use of R Software
12(1)
1.9 Review of Important Concepts
12(3)
1.10 Exercises
15(2)
2 Completely Randomized Designs with One Factor
17(38)
2.1 Introduction
17(1)
2.2 Replication and Randomization
17(2)
2.3 A Historical Example
19(1)
2.4 Linear Model for CRD
20(9)
2.5 Verifying Assumptions of the Linear Model
29(2)
2.6 Analysis Strategies When Assumptions Are Violated
31(7)
2.7 Determining the Number of Replicates
38(3)
2.8 Comparison of Treatments after the F-test
41(8)
2.9 Review of Important Concepts
49(2)
2.10 Exercises
51(4)
3 Factorial Designs
55(58)
3.1 Introduction
55(1)
3.2 Classical One at a Time versus Factorial Plans
55(2)
3.3 Interpreting Interactions
57(3)
3.4 Creating a Two-Factor Factorial Plan in R
60(1)
3.5 Analysis of a Two-Factor Factorial in R
61(19)
3.6 Factorial Designs with Multiple Factors---CRFD
80(5)
3.7 Two-Level Factorials
85(17)
3.8 Verifying Assumptions of the Model
102(4)
3.9 Review of Important Concepts
106(2)
3.10 Exercises
108(5)
4 Randomized Block Designs
113(28)
4.1 Introduction
113(1)
4.2 Creating an RCB in R
114(2)
4.3 Model for RCB
116(1)
4.4 An Example of an RCB
117(4)
4.5 Determining the Number of Blocks
121(1)
4.6 Factorial Designs in Blocks
122(2)
4.7 Generalized Complete Block Design
124(4)
4.8 Two Block Factors LSD
128(5)
4.9 Review of Important Concepts
133(2)
4.10 Exercises
135(5)
4.11 Appendix---Data from the Golf Experiment
140(1)
5 Designs to Study Variances
141(52)
5.1 Introduction
141(1)
5.2 Random Factors and Random Sampling Experiments
142(2)
5.3 One-Factor Sampling Designs
144(1)
5.4 Estimating Variance Components
145(10)
5.5 Two-Factor Sampling Designs
155(9)
5.6 Nested Sampling Experiments (NSE)
164(3)
5.7 Staggered Nested Designs
167(6)
5.8 Designs with Fixed and Random Factors
173(7)
5.9 Graphical Methods to Check Model Assumptions
180(8)
5.10 Review of Important Concepts
188(2)
5.11 Exercises
190(3)
6 Fractional Factorial Designs
193(68)
6.1 Introduction
193(1)
6.2 Half-Fractions of 2k Designs
194(10)
6.3 Quarter and Higher Fractions of 2k Designs
204(2)
6.4 Criteria for Choosing Generators for 2k--p Designs
206(12)
6.5 Augmenting Fractional Factorials
218(11)
6.6 Plackett-Burman (PB) and Model Robust Screening Designs
229(13)
6.7 Mixed Level Factorials and Orthogonal Arrays (OAs)
242(6)
6.8 Definitive Screening Designs
248(2)
6.9 Review of Important Concepts
250(2)
6.10 Exercises
252(9)
7 Incomplete and Confounded Block Designs
261(46)
7.1 Introduction
261(1)
7.2 Balanced Incomplete Block (BIB) Designs
262(2)
7.3 Analysis of Incomplete Block Designs
264(3)
7.4 BTIB and PBIB Designs
267(4)
7.5 Row Column Designs
271(1)
7.6 Confounded 2k and 2k--p Designs
272(13)
7.7 Confounding 3-Level and p-Level Factorial Designs
285(2)
7.8 Blocking Mixed Level Factorials and OAs
287(8)
7.9 Partially Confounded Blocked Factorial (PCBF)
295(5)
7.10 Review of Important Concepts
300(3)
7.11 Exercises
303(4)
8 Split-Plot Designs
307(44)
8.1 Introduction
307(1)
8.2 Split-Plot Experiments with CRD in Whole Plots CRSP
308(7)
8.3 RCB in Whole Plots RBSP
315(8)
8.4 Analysis Unreplicated 2k Split-Plot Designs
323(5)
8.5 2k--p Fractional Factorials in Split Plots (SPFFs)
328(13)
8.6 Sample Size and Power Issues for Split-Plot Designs
341(2)
8.7 Review of Important Concepts
343(2)
8.8 Exercises
345(6)
9 Crossover and Repeated Measures Designs
351(32)
9.1 Introduction
351(1)
9.2 Crossover Designs (CODs)
351(1)
9.3 Simple AB, BA Crossover Designs for Two Treatments
352(9)
9.4 Crossover Designs for Multiple Treatments
361(5)
9.5 Repeated Measures Designs
366(2)
9.6 Univariate Analysis of Repeated Measures Designs
368(9)
9.7 Review of Important Concepts
377(2)
9.8 Exercises
379(4)
10 Response Surface Designs
383(64)
10.1 Introduction
383(1)
10.2 Fundamentals of Response Surface Methodology
383(4)
10.3 Standard Designs for Second Order Models
387(9)
10.4 Creating Standard Response Surface Designs in R
396(4)
10.5 Non-Standard Response Surface Designs
400(7)
10.6 Fitting the Response Surface Model with R
407(6)
10.7 Determining Optimum Operating Conditions
413(13)
10.8 Blocked Response Surface (BRS) Designs
426(4)
10.9 Response Surface Split-Plot (RSSP) Designs
430(9)
10.10 Review of Important Concepts
439(2)
10.11 Exercises
441(6)
11 Mixture Experiments
447(56)
11.1 Introduction
447(2)
11.2 Models and Designs for Mixture Experiments
449(7)
11.3 Creating Mixture Designs in R
456(2)
11.4 Analysis of Mixture Experiments
458(6)
11.5 Constrained Mixture Experiments
464(9)
11.6 Blocking Mixture Experiments
473(5)
11.7 Mixture Experiments with Process Variables
478(9)
11.8 Mixture Experiments in Split-Plot Arrangements
487(4)
11.9 Review of Important Concepts
491(2)
11.10 Exercises
493(8)
11.11 Appendix---Example of Fitting Independent Factors
501(2)
12 Robust Parameter Design Experiments
503(54)
12.1 Introduction
503(1)
12.2 Noise Sources of Functional Variation
504(2)
12.3 Product Array Parameter Design Experiments
506(8)
12.4 Analysis of Product Array Experiments
514(18)
12.5 Single-Array Parameter Design Experiments
532(8)
12.6 Joint Modeling of Mean and Dispersion Effects
540(7)
12.7 Review of Important Concepts
547(2)
12.8 Exercises
549(8)
13 Experimental Strategies for Increasing Knowledge
557(14)
13.1 Introduction
557(1)
13.2 Sequential Experimentation
557(4)
13.3 One-Step Screening and Optimization
561(1)
13.4 An Example of Sequential Experimentation
562(5)
13.5 Evolutionary Operation
567(2)
13.6 Concluding Remarks
569(2)
Appendix---Brief Introduction to R 571(2)
Answers to Selected Exercises 573(4)
Bibliography 577(16)
Index 593
John Lawson is a professor in the Department of Statistics at Brigham Young University.
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