Statistical intervals : a guide for practitioners and researchers / William Q. Meeker, Gerald J. Hahn, Luis A. Escobar.

Format:

• Book

Author/Creator:

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

Series:

• Wiley series in probability and statistics.
• THEi Wiley ebooks.
• Wiley Series in Probability and Statistics
• THEi Wiley ebooks

Language:

English

Subjects (All):

• Mathematical statistics.

Physical Description:

1 online resource (651 pages).

Edition:

2nd ed.

Place of Publication:

Hoboken, New Jersey : Wiley, 2017.

System Details:

Access using campus network via VPN at home (THEi Users Only).

Summary:

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: * Likelihood-based statistical intervals * Nonparametric bootstrap intervals * Parametric bootstrap and other simulation-based 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 up-to-date working guide and reference for all who analyze data, allowing them to quantify the uncertainty in their results using statistical intervals.

Contents:

• Intro
• Statistical Intervals
• Contents
• Preface to Second Edition
• Overview
• Elaboration on New Methods
• New Technical Appendices
• Computer Software
• More on Book's Webpage
• Summary of Changes from First Edition
• Preface to First Edition
• Acknowledgments
• About the Companion Website
• Chapter 1 Introduction, Basic Concepts, and Assumptions
• Objectives and Overview
• 1.1 Statistical Inference
• 1.2 Different Types of Statistical Intervals: An Overview
• 1.3 The Assumption of Sample Data
• 1.4 The Central Role of Practical Assumptions Concerning Representative Data
• 1.5 Enumerative versus Analytic Studies
• 1.5.1 Differentiating between Enumerative and Analytic Studies
• 1.5.2 Statistical Inference for Analytic Studies
• 1.5.3 Inferential versus Predictive Analyses
• 1.6 Basic Assumptions for Inferences from Enumerative Studies
• 1.6.1 Definition of the Target Population and Frame
• 1.6.2 The Assumption of a Random Sample
• 1.6.3 More Complicated Random Sampling Schemes
• 1.7 Considerations in the Conduct of Analytic Studies
• 1.7.1 Analytic Studies
• 1.7.2 The Concept of Statistical Control
• 1.7.3 Other Analytic Studies
• 1.7.4 How to Proceed
• 1.7.5 Planning and Conducting an Analytic Study
• 1.8 Convenience and Judgment Samples
• 1.9 Sampling People
• 1.10 Infinite Population Assumptions
• 1.11 Practical Assumptions: Overview
• 1.12 Practical Assumptions: Further Example
• 1.13 Planning the Study
• 1.14 The Role of Statistical Distributions
• 1.15 The Interpretation of Statistical Intervals
• 1.16 Statistical Intervals and Big Data
• 1.17 Comment Concerning Subsequent Discussion
• BIBLIOGRAPHIC NOTES
• Chapter 2 Overview of Different Types of Statistical Intervals
• 2.1 Choice of a Statistical Interval
• 2.1.1 Purpose of the Interval.
• 2.1.2 Characteristic of Interest
• 2.2 Confidence Intervals
• 2.2.1 Confidence Interval for a Distribution Parameter
• 2.2.2 Confidence Interval for a Distribution Quantile
• 2.2.3 Confidence Interval for the Probability of Meeting Specifications
• 2.2.4 One-Sided Confidence Bounds
• 2.2.5 Interpretations of Confidence Intervals and Bounds
• 2.3 Prediction Intervals
• 2.3.1 Prediction Interval to Contain a Single Future Observation
• 2.3.2 Prediction Interval to Contain All of m Future Observations
• 2.3.3 Prediction Interval to Contain at Least k out of m Future Observations
• 2.3.4 Prediction Interval to Contain the Sample Mean or Sample Standard Deviation of a Future Sample
• 2.3.5 One-Sided Prediction Bounds
• 2.3.6 Interpretation of Prediction Intervals and Bounds
• 2.4 Statistical Tolerance Intervals
• 2.4.1 Tolerance Interval to Contain a Proportion of a Distribution
• 2.4.2 One-Sided Tolerance Bounds
• 2.4.3 Interpretation of β-Content Tolerance Intervals
• 2.4.4 -Expectation Tolerance Intervals
• 2.5 Which Statistical Interval Do I Use?
• 2.6 Choosing a Confidence Level
• 2.6.1 Further Elaboration
• 2.6.2 Problem Considerations
• 2.6.3 Sample Size Considerations
• 2.6.4 A Practical Consideration
• 2.6.5 Further Remarks
• 2.7 Two-Sided Statistical Intervals versus One-Sided Statistical Bounds
• 2.8 The Advantage of Using Confidence Intervals Instead of Significance Tests
• 2.9 Simultaneous Statistical Intervals
• Chapter 3 Constructing Statistical Intervals Assuming a Normal Distribution Using Simple Tabulations
• 3.1 Introduction
• 3.1.1 The Normal Distribution
• 3.1.2 Using the Simple Factors
• 3.2 Circuit Pack Voltage Output Example
• 3.3 Two-Sided Statistical Intervals
• 3.3.1 Simple Tabulations for Two-Sided Statistical Intervals.
• 3.3.2 Two-Sided Interval Examples
• 3.3.3 Comparison of Two-Sided Statistical Intervals
• 3.4 One-Sided Statistical Bounds
• 3.4.1 Simple Tabulations for One-Sided Statistical Bounds
• 3.4.2 One-Sided Statistical Bound Examples
• 3.4.3 Comparison of One-Sided Statistical Bounds
• Chapter 4 Methods for Calculating Statistical Intervals for a Normal Distribution
• 4.1 Notation
• 4.2 Confidence Interval for the Mean of A Normal Distribution
• 4.3 Confidence Interval for The Standard Deviation of a Normal Distribution
• 4.4 Confidence Interval for a Normal Distribution Quantile
• 4.5 Confidence Interval for the Distribution Proportion Less (Greater) than a Specified Value
• 4.6 Statistical Tolerance Intervals
• 4.6.1 Two-Sided Tolerance Interval to Control the Center of a Distribution
• 4.6.2 Two-Sided Tolerance Interval to Control Both Tails of a Distribution
• 4.6.3 One-Sided Tolerance Bounds
• 4.7 Prediction Interval to Contain a Single Future Observation or the Mean of m Future Observations
• 4.8 Prediction Interval to Contain at Least k of m Future Observations
• 4.8.1 Two-Sided Prediction Interval
• 4.8.2 One-Sided Prediction Bounds
• 4.9 Prediction Interval to Contain the Standard Deviation of m Future Observations
• 4.10 The Assumption of a Normal Distribution
• 4.11 Assessing Distribution Normality and Dealing with Nonnormality
• 4.11.1 Probability Plots and Q
• Q Plots
• 4.11.2 Interpreting Probability Plots and Q
• 4.11.3 Dealing with Nonnormal Data
• 4.12 Data Transformations and Inferences from Transformed Data
• 4.12.1 Power Transformations
• 4.12.2 Computing Statistical Intervals from Transformed Data
• 4.12.3 Comparison of Inferences Using Different Transformations
• 4.12.4 Box
• Cox Transformations
• 4.13 Statistical Intervals for Linear Regression Analysis.
• 4.13.1 Confidence Intervals for Linear Regression Analysis
• 4.13.2 Tolerance Intervals for Linear Regression Analysis
• 4.13.3 Prediction Intervals for Regression Analysis
• 4.14 Statistical Intervals for Comparing Populations and Processes
• Bibliographic Notes
• Chapter 5 Distribution-Free Statistical Intervals
• 5.1 Introduction
• 5.1.1 Motivation
• 5.1.2 Notation
• 5.2 Distribution-Free Confidence Intervals and One-Sided Confidence Bounds for a Quantile
• 5.2.1 Coverage Probabilities for Distribution-Free Confidence Intervals or One-Sided Confidence Bounds for a Quantile
• 5.2.2 Using Interpolation to Obtain Approximate Distribution-Free Confidence Bounds or Confidence Intervals for a Quantile
• 5.2.3 Distribution-Free One-Sided Upper Confidence Bounds for a Quantile
• 5.2.4 Distribution-Free One-Sided Lower Confidence Bounds for a Quantile
• 5.2.5 Distribution-Free Two-Sided Confidence Interval for a Quantile
• 5.3 Distribution-Free Tolerance Intervals and Bounds to Contain a Specified Proportion of a Distribution
• 5.3.1 Distribution-Free Two-Sided Tolerance Intervals
• 5.3.2 Distribution-Free One-Sided Tolerance Bounds
• 5.3.3 Minimum Sample Size Required for Constructing a Distribution-Free Two-Sided Tolerance Interval
• 5.4 Prediction Intervals and Bounds to Contain a Specified Ordered Observation in a Future Sample
• 5.4.1 Coverage Probabilities for Distribution-Free Prediction Intervals and One-Sided Prediction Bounds for a Particular Ordered Observation
• 5.4.2 Distribution-Free One-Sided Upper Prediction Bound for Y(j)
• 5.4.3 Distribution-Free One-Sided Lower Prediction Bound for Y(j)
• 5.4.4 Distribution-Free Two-Sided Prediction Interval for Y(j)
• 5.5 Distribution-Free Prediction Intervals and Bounds to Contain at Least k of m Future Observations.
• 5.5.1 Distribution-Free Two-Sided Prediction Intervals to Contain at Least k of m Future Observations
• 5.5.2 Distribution-Free One-Sided Prediction Bounds to Exceed or Be Exceeded by at Least k of m Future Observations
• Chapter 6 Statistical Intervals for a Binomial Distribution
• 6.1 Introduction
• 6.1.1 The Binomial Distribution
• 6.1.2 Other Distributions and Related Notation
• 6.1.3 Notation for Data and Inference
• 6.1.4 Binomial Distribution Statistical Interval Properties
• 6.1.5 Two Examples, Motivation, and a Caution
• 6.2 Confidence Intervals for the Actual Proportion Nonconforming in the Sampled Distribution
• 6.2.1 Preliminaries
• 6.2.2 The Conservative Method
• 6.2.3 The Wald (Normal Theory) Approximate Method
• 6.2.4 The Agresti
• Coull Adjusted Wald-Approximation Method
• 6.2.5 The Jeffreys Approximate Method
• 6.2.6 Comparisons and Recommendations
• 6.3 Confidence Interval for the Proportion of Nonconforming Units in a Finite Population
• 6.3.1 The Conservative Method
• 6.3.2 Large-Population Approximate Method
• 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
• 6.5 Confidence Intervals for the Quantile of the Distribution of the Number of Nonconforming Units
• 6.5.1 Two-Sided Confidence Interval for yp
• 6.5.2 One-Sided Confidence Bounds for yp
• 6.6 Tolerance Intervals and One-Sided Tolerance Bounds for the Distribution of the Number of Nonconforming Units
• 6.6.1 One-Sided Lower Tolerance Bound for a Binomial Distribution
• 6.6.2 One-Sided Upper Tolerance Bound for a Binomial Distribution
• 6.6.3 Two-Sided Tolerance Interval for a Binomial Distribution
• 6.6.4 Calibrating Tolerance Intervals.
• 6.7 Prediction Intervals for the Number Nonconforming in a Future Sample.

Notes:

• Includes bibliographical references at the end of each chapters and index.
• Description based on print version record.

ISBN:

• 9781118595169
• 1118595165
• 9781118594957
• 1118594959
• 9781118594841
• 1118594843

OCLC:

975486990