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E-grāmata: Fundamentals of Statistical Experimental Design and Analysis

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  • Formāts: PDF+DRM
  • Izdošanas datums: 23-Oct-2015
  • Izdevniecība: John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781118954652
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Fundamentals of Statistical Experimental Design and AnalysisPalielināt
  • Formāts: PDF+DRM
  • Izdošanas datums: 23-Oct-2015
  • Izdevniecība: John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781118954652
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Professionals in all areas business; government; the physical, life, and social sciences; engineering; medicine, etc. benefit from using statistical experimental design to better understand their worlds and then use that understanding to improve the products, processes, and programs they are responsible for. This book aims to provide the practitioners of tomorrow with a memorable, easy to read, engaging guide to statistics and experimental design.

This book uses examples, drawn from a variety of established texts, and embeds them in a business or scientific context, seasoned with a dash of humor, to emphasize the issues and ideas that led to the experiment and the what-do-we-do-next? steps after the experiment. Graphical data displays are emphasized as means of discovery and communication and formulas are minimized, with a focus on interpreting the results that software produce. The role of subject-matter knowledge, and passion, is also illustrated. The examples do not require specialized knowledge, and the lessons they contain are transferrable to other contexts.  

Fundamentals of Statistical Experimental Design and Analysis introduces the basic elements of an experimental design, and the basic concepts underlying statistical analyses. Subsequent chapters address the following families of experimental designs:









Completely Randomized designs, with single or multiple treatment factors, quantitative or qualitative Randomized Block designs Latin Square designs Split-Unit designs Repeated Measures designs Robust designs Optimal designs



Written in an accessible, student-friendly style, this book is suitable for a general audience and particularly for those professionals seeking to improve and apply their understanding of experimental design.

Recenzijas

This is an interesting and very useful book that explains the basic concepts and fundamentals of statistical experimental design and analysis to its readers in an easy-to-understand and accessible style Rather than presenting the statistical design of experiments as a cut-and-dried subject, the author spices up this book with a sense of humour and fun.. Students, professionals, and researchers will find it interesting. It is a welcome addition to the statistics market D. V. Chopra, MathSciNet, Aug 2017

Easterling sets out to provide a textbook for an undergraduate course in applied experimental design for a mixed group of students. He succeeds admirably. Although many excellent texts on experimental design exist for statistics students, most are too technical for mixed disciplines. This book covers only basic designs but with extensive discussion of matters other textbooks elide or ignore. Examples from respected textbooks are elaborated to show the reasoning underpinning experimentation and the need to combine statistical and subject-area knowledge this is a book that can be enjoyed by students being taught how and why to work with a statistician, and by statisticians who want to work more productively in teams with other disciplines. Significance, 14:6 (2017)

Preface xiii
Acknowledgments xix
Credits xxi
1 Introduction 1(10)
Motivation: Why Experiment?
1(1)
Steps in an Experimental Program
2(2)
Planning and analysis
2(1)
Communication
3(1)
Subject-Matter Passion
4(1)
Case Study
5(4)
Overview of Text
9(1)
Assignment
10(1)
References
10(1)
2 Fundamentals of Experimental Design 11(18)
Introduction
11(2)
Experimental Structure
13(7)
Experimental units
13(2)
Blocks and block structures
15(2)
Treatments and treatment structures
17(2)
Response measurement
19(1)
Principles of Experimental Design
20(7)
Replication
21(1)
Randomization
22(2)
Blocking
24(2)
Control
26(1)
Assignment
27(1)
References
27(2)
3 Fundamentals of Statistical Data Analysis 29(62)
Introduction
29(1)
Boys' Shoes Experiment
30(26)
Experimental design
30(1)
Graphical displays
31(3)
Significance testing
34(1)
Probability and probability distributions
34(2)
Sign test
36(2)
Misinterpretation of P-values
38(1)
Randomization test
39(1)
Normal distribution theory t-test
40(6)
Summary and discussion: Significance tests
46(2)
Economic analysis: The bigger picture
48(2)
Statistical confidence intervals
50(3)
Discussion
53(1)
Why calculate statistical confidence limits?
54(1)
Sample size determination
54(2)
Tomato Fertilizer Experiment
56(3)
Experimental design
56(1)
Analysis 1: Plot the data
56(2)
The value of randomization
58(1)
The importance of ancillary data
59(1)
A New Tomato Experiment
59(18)
Analysis 1: Plot the data
59(3)
Significance tests
62(1)
Rank sum test
63(1)
Randomization test
64(2)
Normal theory t-test
66(3)
Confidence intervals
69(2)
Determining the size of an experiment
71(6)
Comparing Standard Deviations
77(2)
Discussion
79(1)
Appendix 3.A The Binomial Distribution
79(2)
Appendix 3.B Sampling from a Normal Distribution
81(4)
Appendix 3.C Statistical Underpinnings
85(4)
Single sample
86(1)
Two samples
87(2)
Assignment
89(1)
References
89(2)
4 Completely Randomized Design 91(32)
Introduction
91(1)
Design Issues
92(1)
CRD: Single Qualitative Factor
92(3)
Example: Market research
92(3)
Analysis of Variance
95(8)
Within-group variation
96(1)
Among-groups variation
97(1)
The F-test
98(1)
Analysis of variance
99(1)
Discussion
100(1)
Results
101(2)
Testing the Assumptions of Equal Variances and Normality
103(1)
Confidence Intervals
103(2)
Inference
105(1)
Statistical Prediction Interval
105(1)
Example: Tomato Fertilizer Experiment Revisited
106(1)
Sizing a Completely Randomized Experiment
107(1)
CRD: Single Quantitative Factor
107(6)
Example: Growth rate of rats
108(1)
Graphical display
109(1)
Curve fit
109(2)
Analysis of variance
111(2)
Design Issues
113(1)
Enhanced Case Study: Power Window Gear Teeth
114(6)
Graphical display
117(2)
ANOVA
119(1)
Discussion
120(1)
Assignment
120(1)
References
121(2)
5 Completely Randomized Design with Multiple Treatment Factors 123(54)
Introduction
123(1)
Design Issues
124(1)
Example 1 (Two qualitative factors): Poisons and antidotes
124(25)
Analysis 1: Plot the data
126(1)
Eyeball analysis
126(2)
Interaction
128(2)
ANOVA
130(1)
Generalizing the ANOVA for a CRD with two factors
131(1)
Antidote B versus Antidote D
132(1)
Estimation of effects
133(2)
Prediction intervals
135(1)
Probability estimation and tolerance intervals
136(2)
Further experiments
138(1)
Example 2 (Two quantitative factors): Ethanol blends and CO emissions
139(3)
Data displays
142(2)
Discussion
144(1)
Regression analysis and ANOVA
145(3)
Discussion
148(1)
Response Surface Designs
149(6)
Extensions: More than two treatment factors
150(1)
Example 3: Poison/antidote experiment extended
151(3)
Example 4: Ethanol experiment extended
154(1)
Special Case: Two-Level Factorial Experiments
155(12)
Example 5: Pot production
156(2)
Analysis 1: Look at the data
158(1)
Analysis 2: Regression analysis
159(3)
Analysis 2: Stepwise regression
162(1)
Analysis 3: "Effect sparsity" and graphical analysis
162(5)
Fractional Two-Level Factorials
167(8)
Example 6: E-mail marketing
167(1)
One-factor-at-a-time designs
168(2)
Results: E-mail experiment
170(1)
Example 7: Flower pot experiment revisited
171(4)
Extensions
175(1)
Assignment
175(1)
References
175(2)
6 Randomized Complete Block Design 177(30)
Introduction
177(1)
Design Issues
178(10)
RBD with replication: Example 1—battery experiment
179(1)
Analysis 1: Plot the data
180(1)
Analysis of variance
181(2)
Reliability analysis
183(1)
Further analysis
184(1)
Bringing subject-matter knowledge to bear
185(2)
Example 2: More tomato fertilizer experiments
187(1)
Example 3: More gear teeth experiments
188(1)
RBD with Single Replication
188(6)
Example 4: Penicillin production
189(2)
Components of variation
191(3)
Sizing a Randomized Block Experiment
194(1)
True Replication
195(4)
Example 5: Cookies
195(1)
Example 6: Battery experiment revisited
196(1)
Example 7: Boys' shoes revisited
197(2)
Extensions of the RBD
199(3)
Multifactor treatments and blocks-example: Penicillin experiment extended
199(2)
Example 8: A blocks-only "experiment"-textile production
201(1)
Analysis 1: Plot the data
201(1)
Discussion
202(1)
Balanced Incomplete Block Designs
203(2)
Example: Boys' shoes revisited again
203(2)
Summary
205(1)
Assignment
205(1)
References
205(2)
7 Other Experimental Designs 207(38)
Introduction
207(1)
Latin Square Design
208(10)
Example: Gasoline additives and car emissions
208(4)
Analysis 1: Plot the data
212(2)
ANOVA
214(1)
Discussion
215(1)
Follow-on experiments
216(1)
Exercise
216(1)
Extensions
217(1)
Split-Unit Designs
218(12)
Example: Corrosion Resistance
220(2)
Analysis 1: Plot the data
222(3)
ANOVA
225(3)
Discussion
228(2)
Repeated Measures Designs
230(5)
Example: Effects of drugs on heart rate
231(1)
Analysis 1: Plot the data
232(2)
Discussion
234(1)
Extensions
235(1)
Robust Designs
235(5)
Introduction
235(1)
Variance transmission
235(3)
Mathematical model: Robustness
238(1)
Concluding comments
239(1)
Optimal Designs
240(3)
Introduction
240(1)
Finding "optimal experimental designs"
240(2)
Design augmentation
242(1)
Assignment
243(1)
References
243(2)
Index 245
Robert G. Easterling. Dr. Easterling is retired from Sandia National Laboratories where he was a statistical consultant, manager, and senior scientist. He is a Fellow of the American Statistical Association, a former Editor of Technometrics, and a recipient of the American Society for Qualitys Brumbaugh Award. He holds a Ph.D. in statistics from Oklahoma State University.
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