Statistical Intervals: A Guide for Practitioners and Researchers, 2nd EditionISBN: 9780471687177
648 pages
April 2017

Description
Describes statistical intervals to quantify sampling uncertainty,focusing on key application needs and recently developed methodology in an easytoapply format
Statistical intervals provide invaluable tools for quantifying sampling uncertainty. The widely hailed first edition, published in 1991, described the use and construction of the most important statistical intervals. Particular emphasis was given to intervals—such as prediction intervals, tolerance intervals and confidence intervals on distribution quantiles—frequently needed in practice, but often neglected in introductory courses.
Vastly improved computer capabilities over the past 25 years have resulted in an explosion of the tools readily available to analysts. This second edition—more than double the size of the first—adds these new methods in an easytoapply format. In addition to extensive updating of the original chapters, the second edition includes new chapters on:
 Likelihoodbased statistical intervals
 Nonparametric bootstrap intervals
 Parametric bootstrap and other simulationbased intervals
 An introduction to Bayesian intervals
 Bayesian intervals for the popular binomial, Poisson and normal distributions
 Statistical intervals for Bayesian hierarchical models
 Advanced case studies, further illustrating the use of the newly described methods
New technical appendices provide justification of the methods and pathways to extensions and further applications. A webpage directs readers to current readily accessible computer software and other useful information.
Statistical Intervals: A Guide for Practitioners and Researchers, Second Edition is an uptodate working guide and reference for all who analyze data, allowing them to quantify the uncertainty in their results using statistical intervals.
Table of Contents
Preface to First Edition vii
Acknowledgments x
1 Introduction, Basic Concepts, and Assumptions 1
1.1 Statistical Inference 2
1.2 Different Types of Statistical Intervals: An Overview 2
1.3 The Assumption of Sample Data 3
1.4 The Central Role of Practical Assumptions Concerning Representative Data 4
1.5 Enumerative Versus Analytic Studies 5
1.6 Basic Assumptions for Enumerative Studies 7
1.7 Considerations in the Conduct of Analytic Studies 10
1.8 Convenience and Judgment Samples 11
1.9 Sampling People 12
1.10 Infinite Population Assumptions 13
1.11 Practical Assumptions: Overview 14
1.12 Practical Assumptions: Further Example 14
1.13 Planning the Study 17
1.14 The Role of Statistical Distributions 17
1.15 The Interpretation of Statistical Intervals 18
1.16 Statistical Intervals and Big Data 19
1.17 Comment Concerning Subsequent Discussion 19
2 Overview of Different Types of Statistical Intervals 21
2.1 Choice of a Statistical Interval 21
2.2 Confidence Intervals 23
2.3 Prediction Intervals 24
2.4 Statistical Tolerance Intervals 26
2.5 Which Statistical Interval Do I Use? 27
2.6 Choosing a Confidence Level 28
2.7 TwoSided Statistical Intervals Versus OneSided Statistical Bounds 29
2.8 The Advantage of Using Confidence Intervals Instead of Significance Tests 30
2.9 Simultaneous Statistical Intervals 31
3 Constructing Statistical Intervals Assuming a Normal Distribution Using Simple Tabulations 33
3.1 Introduction 34
3.2 Circuit Pack Voltage Output Example 35
3.3 TwoSided Statistical Intervals 36
3.4 OneSided Statistical Bounds 38
4 Methods for Calculating Statistical Intervals for a Normal Distribution 43
4.1 Notation 44
4.2 Confidence Interval for the Mean of a Normal Distribution 45
4.3 Confidence Interval for the Standard Deviation of a Normal Distribution 45
4.4 Confidence Interval for a Normal Distribution Quantile 46
4.5 Confidence Interval for the Distribution Proportion Less (Greater) Than a Specified Value 47
4.6 Statistical Tolerance Intervals 48
4.7 Prediction Interval to Contain a Single Future Observation or the Mean of m Future Observations 50
4.8 Prediction Interval to Contain at least k of m Future Observations 51
4.9 Prediction Interval to Contain the Standard Deviation of m Future Observations 52
4.10 The Assumption of a Normal Distribution 53
4.11 Assessing Distribution Normality and Dealing with Nonnormality 54
4.12 Data Transformations and Inferences from Transformed Data 57
4.13 Statistical Intervals for Linear Regression Analysis 60
4.14 Statistical Intervals for Comparing Populations and Processes 62
5 DistributionFree Statistical Intervals 65
5.1 Introduction 66
5.2 DistributionFree Confidence Intervals and OneSided Confidence Bounds for a Quantile 68
5.3 DistributionFree Tolerance Intervals and Bounds to Contain a Specified Proportion of a Distribution 78
5.4 Prediction Intervals to Contain a Specified Ordered Observation in a Future Sample 81
5.5 DistributionFree Prediction Intervals and Bounds to Contain at Least k of m Future Observations 84
6 Statistical Intervals for a Binomial Distribution 89
6.1 Introduction to Binomial Distribution Statistical Intervals 90
6.2 Confidence Intervals for the Actual Proportion Nonconforming in the Sampled Distribution 92
6.3 Confidence Interval for the Proportion of Nonconforming Units in a Finite Population 102
6.4 Confidence Intervals for the Probability that the Number of Nonconforming Units in a Sample is Less than or Equal to (or Greater than) a Specified Number 104
6.5 Confidence Intervals for the Quantile of the Distribution of the Number of Nonconforming Units 105
6.6 Tolerance Intervals and OneSided Tolerance Bounds for the Distribution of the Number of Nonconforming Units 107
6.7 Prediction Intervals for the Number Nonconforming in a Future Sample 108
7 Statistical Intervals for a Poisson Distribution 115
7.1 Introduction 116
7.2 Confidence Intervals for the EventOccurrence Rate of a Poisson Distribution 117
7.3 Confidence Intervals for the Probability that the Number of Events in a Specified Amount of Exposure is Less than or Equal to (or Greater than) a Specified Number 124
7.4 Confidence Intervals for the Quantile of the Distribution of the Number of Events in a Specified Amount of Exposure 125
7.5 Tolerance Intervals and OneSided Tolerance Bounds for the Distribution of the Number of Events in a Specified Amount of Exposure 127
7.6 Prediction Intervals for the Number of Events in a Future Amount of Exposure 128
8 Sample Size Requirements for Confidence Intervals on Distribution Parameters 135
8.1 Basic Requirements for Sample Size Determination 136
8.2 Sample Size for a Confidence Interval for a Normal Distribution Mean 137
8.3 Sample Size to Estimate a Normal Distribution Standard Deviation 141
8.4 Sample Size to Estimate a Normal Distribution Quantile 143
8.5 Sample Size to Estimate a Binomial Proportion 143
8.6 Sample Size to Estimate a Poisson Occurrence Rate 144
9 Sample Size Requirements for Tolerance Intervals, Tolerance Bounds, and Related Demonstration Tests 148
9.1 Sample Size for Normal Distribution Tolerance Intervals and OneSided Tolerance Bounds148
9.2 Sample Size to Pass a OneSided Demonstration Test Based on Normally Distributed Measurements 150
9.3 Minimum Sample Size For DistributionFree TwoSided Tolerance Intervals and OneSided Tolerance Bounds 152
9.4 Sample Size for Controlling the Precision of TwoSided DistributionFree Tolerance Intervals and OneSided DistributionFree Tolerance Bounds 153
9.5 Sample Size to Demonstrate that a Binomial Proportion Exceeds (is Exceeded by) a Specified Value 154
10 Sample Size Requirements for Prediction Intervals 164
10.1 Prediction Interval Width: The Basic Idea 164
10.2 Sample Size for a Normal Distribution Prediction Interval 165
10.3 Sample Size for DistributionFree Prediction Intervals for k of m Future Observations 170
11 Basic Case Studies 172
11.1 Demonstration that the Operating Temperature of Most Manufactured Devices will not Exceed a Specified Value 173
11.2 Forecasting Future Demand for Spare Parts 177
11.3 Estimating the Probability of Passing an Environmental Emissions Test 180
11.4 Planning a Demonstration Test to Verify that a Radar System has a Satisfactory Probability of Detection 182
11.5 Estimating the Probability of Exceeding a Regulatory Limit 184
11.6 Estimating the Reliability of a Circuit Board 189
11.7 Using Sample Results to Estimate the Probability that a Demonstration Test will be Successful 191
11.8 Estimating the Proportion within Specifications for a TwoVariable Problem 194
11.9 Determining the Minimum Sample Size for a Demonstration Test 195
12 LikelihoodBased Statistical Intervals 197
12.1 Introduction to LikelihoodBased Inference 198
12.2 Likelihood Function and Maximum Likelihood Estimation 200
12.3 LikelihoodBased Confidence Intervals for SingleParameter Distributions 203
12.4 LikelihoodBased Estimation Methods for LocationScale and LogLocationScale Distributions 206
12.5 LikelihoodBased Confidence Intervals for Parameters and Scalar Functions of Parameters212
12.6 WaldApproximation Confidence Intervals 216
12.7 Some Other LikelihoodBased Statistical Intervals 224
13 Nonparametric Bootstrap Statistical Intervals 226
13.1 Introduction 227
13.2 Nonparametric Methods for Generating Bootstrap Samples and Obtaining Bootstrap Estimates 227
13.3 Bootstrap Operational Considerations 231
13.4 Nonparametric Bootstrap Confidence Interval Methods 233
14 Parametric Bootstrap and Other SimulationBased Statistical Intervals 245
14.1 Introduction 246
14.2 Parametric Bootstrap Samples and Bootstrap Estimates 247
14.3 Bootstrap Confidence Intervals Based on Pivotal Quantities 250
14.4 Generalized Pivotal Quantities 253
14.5 SimulationBased Tolerance Intervals for LocationScale or LogLocationScale Distributions 258
14.6 SimulationBased Prediction Intervals and OneSided Prediction Bounds for k of m Future Observations from LocationScale or LogLocationScale Distributions 260
14.7 Other Simulation and Bootstrap Methods and Application to Other Distributions and Models 263
15 Introduction to Bayesian Statistical Intervals 270
15.1 Bayesian Inference: Overview 271
15.2 Bayesian Inference: an Illustrative Example 274
15.3 More About Specification of a Prior Distribution 283
15.4 Implementing Bayesian Analyses Using Markov Chain Monte Carlo Simulation 286
15.5 Bayesian Tolerance and Prediction Intervals 291
16 Bayesian Statistical Intervals for the Binomial, Poisson and Normal Distributions 297
16.1 Bayesian Intervals for the Binomial Distribution 298
16.2 Bayesian Intervals for the Poisson Distribution 306
16.3 Bayesian Intervals for the Normal Distribution 311
17 Statistical Intervals for Bayesian Hierarchical Models 321
17.1 Bayesian Hierarchical Models and Random Effects 322
17.2 Normal Distribution Hierarchical Models 323
17.3 Binomial Distribution Hierarchical Models 325
17.4 Poisson Distribution Hierarchical Models 328
17.5 Longitudinal Repeated Measures Models 329
18 Advanced Case Studies 335
18.1 Confidence Interval for the Proportion of Defective Integrated Circuits 336
18.2 Confidence Intervals for Components of Variance in a Measurement Process 339
18.3 Tolerance Interval to Characterize the Distribution of Process Output in the Presence of Measurement Error 344
18.4 Confidence Interval for the Proportion of Product Conforming to a TwoSided Specification345
18.5 Confidence Interval for the Treatment Effect in a Marketing Campaign 348
18.6 Confidence Interval for the Probability of Detection with Limited HitMiss Data 349
18.7 Using Prior Information to Estimate the ServiceLife Distribution of a Rocket Motor 353
Epilogue 357
A Notation and Acronyms 360
B Generic Definition of Statistical Intervals and Formulas for Computing Coverage Probabilities 367
B.1 Introduction 367
B.2 Twosided Confidence Intervals and Onesided Confidence Bounds for Distribution Parameters or a Function of Parameters 368
B.3 Two Sided ControltheCenter Tolerance Intervals to Contain at Least a Specified Proportion of a Distribution 371
B.4 Two Sided Tolerance Intervals to Control Both Tails of a Distribution 374
B.5 OneSided Tolerance Bounds 377
B.6 Twosided Prediction Intervals and OneSided Prediction Bounds for Future Observations378
B.7 TwoSided Simultaneous Prediction Intervals and OneSided Simultaneous Prediction Bounds 381
B.8 Calibration of Statistical Intervals 383
C Useful Probability Distributions 384
C.1 Probability Distribution and R Computations 384
C.2 Important Characteristics of Random Variables 385
C.3 Continuous Distributions 388
C.4 Discrete Distributions 398
D General Results from Statistical Theory and Some Methods Used to Construct Statistical Intervals 404
D.1 cdfs and pdfs of Functions of Random Variables 405
D.2 Statistical Error Propagation—The Delta Method 409
D.3 Likelihood and Fisher Information Matrices 410
D.4 Convergence in Distribution 413
D.5 Outline of General ML Theory 415
D.6 The CDF pivotal method for constructing confidence intervals 419
D.7 Bonferroni approximate statistical intervals 424
E Pivotal Methods for Constructing Parametric Statistical Intervals 427
E.1 General definition and examples of pivotal quantities 428
E.2 Pivotal Quantities for the Normal Distribution 428
E.3 Confidence intervals for a Normal Distribution Based on Pivotal Quantities 429
E.4 Confidence Intervals for Two Normal Distributions Based on Pivotal Quantities 432
E.5 Tolerance Intervals for a Normal Distribution Based on Pivotal Quantities 432
E.6 Normal Distribution Prediction Intervals Based on Pivotal Quantities 434
E.7 Pivotal Quantities for LogLocationScale Distributions 436
F Generalized Pivotal Quantities 440
F.1 Definition of Generalized Pivotal Quantities 440
F.2 A Substitution Method to Obtain GPQs 441
F.3 Examples of GPQs for Functions of LocationScale Distribution Parameters 441
F.4 Conditions for Exact Intervals Derived from GPQs 443
G DistributionFree Intervals Based on Order Statistics 446
G.1 Basic Statistical Results Used in this Appendix 446
G.2 DistributionFree Confidence Intervals and Bounds for a Distribution Quantile 447
G.3 DistributionFree Tolerance Intervals to Contain a Given Proportion of a Distribution 448
G.4 DistributionFree Prediction Interval to Contain a Specified Ordered Observation From a Future Sample 449
G.5 DistributionFree Prediction Intervals and Bounds to Contain at Least k of m Future Observations From a Future Sample 451
H Basic Results from Bayesian Inference Models 455
H.1 Basic Statistical Results Used in this Appendix 455
H.2 Bayes’ Theorem 456
H.3 Conjugate Prior Distributions 456
H.4 Jeffreys Prior Distributions 459
H.5 Posterior Predictive Distributions 463
H.6 Posterior Predictive Distributions Based on Jeffreys Prior Distributions 465
I Probability of Successful Demonstration 468
I.1 Demonstration Tests Based on a Normal Distribution Assumption 468
I.2 DistributionFree Demonstration Tests 469
J Tables 471
References 508
Subject Index 525
Author Information
William Q. Meeker is Professor of Statistics and Distinguished Professor of Liberal Arts and Sciences at Iowa State University. He is coauthor of Statistical Methods for Reliability Data (Wiley, 1998) and of numerous publications in the engineering and statistical literature and has won many awards for his research.
Gerald J. Hahn served for 46 years as applied statistician and manager of an 18person statistics group supporting General Electric and has coauthored four books. His accomplishments have been recognized by GE's prestigious Coolidge Fellowship and 19 professional society awards.
Luis A. Escobar is Professor of Statistics at Louisiana State University. He is coauthor of Statistical Methods for Reliability Data (Wiley, 1998) and several book chapters. His publications have appeared in the engineering and statistical literature and he has won several research and teaching awards.