Gerald J. Hahn, William Q. Meeker, Luis A. Escobar

Statistical Intervals

A Guide for Practitioners and Researchers

• Gebundenes Buch

Produktbeschreibung

Statistical Intervals is a guide for practitioners and researchers--providing a detailed, comprehensive, modernized treatment of this important subject. With numerous examples, it presents and differentiates in an easy-to-apply manner the use of confidence intervals (e.g.

Produktdetails

• Wiley Series in Probability and Statistics
• Verlag: John Wiley and Sons Ltd
• 2 Revised edition
• Seitenzahl: 648
• Erscheinungstermin: 10. April 2017
• Englisch
• Abmessung: 263mm x 187mm x 38mm
• Gewicht: 1294g
• ISBN-13: 9780471687177
• ISBN-10: 0471687170
• Artikelnr.: 47489352

Autorenporträt

William Q. Meeker is Professor of Statistics and Distinguished Professor of Liberal Arts and Sciences at Iowa State University. He is the co-author of Statistical Methods for Reliability Data, 2nd Edition (Wiley, 2021) 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 18-person statistics group supporting General Electric and has co-authored 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 the co-author of Statistical Methods for Reliability Data, 2nd Edition (Wiley, 2021) and several book chapters. His publications have appeared in the engineering and statistical literature and he has won several research and teaching awards.

Inhaltsangabe

Preface to Second Edition iii 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 Two-Sided Statistical Intervals Versus
One-Sided 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 Two-Sided Statistical Intervals 36 3.4
One-Sided 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 Distribution-Free Statistical
Intervals 65 5.1 Introduction 66 5.2 Distribution-Free Confidence Intervals
and One-Sided Confidence Bounds for a Quantile 68 5.3 Distribution-Free
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 Distribution-Free 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 One-Sided 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
Event-Occurrence 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 One-Sided 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 One-Sided Tolerance Bounds148 9.2
Sample Size to Pass a One-Sided Demonstration Test Based on Normally
Distributed Measurements 150 9.3 Minimum Sample Size For Distribution-Free
Two-Sided Tolerance Intervals and One-Sided Tolerance Bounds 152 9.4 Sample
Size for Controlling the Precision of Two-Sided Distribution-Free Tolerance
In-tervals and One-Sided Distribution-Free 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
Distribution-Free 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
Prob-ability 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 Two-Variable Problem 194 11.9 Determining the
Minimum Sample Size for a Demonstration Test 195 12 Likelihood-Based
Statistical Intervals 197 12.1 Introduction to Likelihood-Based Inference
198 12.2 Likelihood Function and Maximum Likelihood Estimation 200 12.3
Likelihood-Based Confidence Intervals for Single-Parameter Distributions
203 12.4 Likelihood-Based Estimation Methods for Location-Scale and
Log-Location-Scale Distri-butions 206 12.5 Likelihood-Based Confidence
Intervals for Parameters and Scalar Functions of Parameters212 12.6
Wald-Approximation Confidence Intervals 216 12.7 Some Other
Likelihood-Based 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
Simulation-Based 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 Simulation-Based Tolerance Intervals for
Location-Scale or Log-Location-Scale Distribu-tions 258 14.6
Simulation-Based Prediction Intervals and One-Sided Prediction Bounds for k
of m Fu-ture Observations from Location-Scale or Log-Location-Scale
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
Two-Sided 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 Hit-Miss Data 349 18.7 Using Prior
Information to Estimate the Service-Life 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 Two-sided Confidence Intervals and One-sided
Confidence Bounds for Distribution Pa-rameters or a Function of Parameters
368 B.3 Two Sided Control-the-Center Tolerance Intervals to Contain at
Least a Specified Pro-portion of a Distribution 371 B.4 Two Sided Tolerance
Intervals to Control Both Tails of a Distribution 374 B.5 One-Sided
Tolerance Bounds 377 B.6 Two-sided Prediction Intervals and One-Sided
Prediction Bounds for Future Observations378 B.7 Two-Sided Simultaneous
Prediction Intervals and One-Sided 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 Sta-tistical 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 Log-Location-Scale 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 Location-Scale Distribution Parameters 441 F.4
Conditions for Exact Intervals Derived from GPQs 443 G Distribution-Free
Intervals Based on Order Statistics 446 G.1 Basic Statistical Results Used
in this Appendix 446 G.2 Distribution-Free Confidence Intervals and Bounds
for a Distribution Quantile 447 G.3 Distribution-Free Tolerance Intervals
to Contain a Given Proportion of a Distribution 448 G.4 Distribution-Free
Prediction Interval to Contain a Specified Ordered Observation From a
Future Sample 449 G.5 Distribution-Free 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 Distribution-Free Demonstration Tests 469 J Tables 471
References 508 Subject Index 525