William Q. Meeker, Gerald J. Hahn, Luis A. Escobar
CHF87.00
Download steht sofort bereit
Kein Rückgaberecht
Describes statistical intervals to quantify sampling uncertainty,focusing on key application needs and recently developed methodology in an easy-to-apply 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 easy-to-apply format. In addition to extensive updating of the original chapters, the second edition includes new chapters on:
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 up-to-date working guide and reference for all who analyze data, allowing them to quantify the uncertainty in their results using statistical intervals.
Autorentext
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.
Inhalt
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 …