There is a very simple and fundamental concept· to much of probability and statistics that can be conveyed using the following problem. PROBLEM. Assume a finite set (universe) of N units where A of the units have a particular attribute. The value of N is known while the value of A is unknown. If a proper subset (sample) of size n is selected randomly and a of the units in the subset are observed to have the particular attribute, what can be said about the unknown value of A? The problem is not new and almost anyone can describe several situations where a particular problem could be presented in this setting. Some recent references with different focuses include Cochran (1977); Williams (1978); Hajek (1981); Stuart (1984); Cassel, Samdal, and Wretman (1977); and Johnson and Kotz (1977). We focus on confidence interval estimation of A. Several methods for exact confidence interval estimation of A exist (Buonaccorsi, 1987, and Peskun, 1990), and this volume presents the theory and an extensive Table for one of them. One of the important contributions in Neyman (1934) is a discussion of the meaning of confidence interval estimation and its relationship with hypothesis testing which we will call the Neyman Approach. In Chapter 3 and following Neyman's Approach for simple random sampling (without replacement), we present an elementary development of exact confidence interval estimation of A as a response to the specific problem cited above.

Klappentext

This book is an extensive and easy to use reference for students and practitioners for finding exact confidence intervals when sampling from finite populations. It can be used by statisticians, engineers, life, physical, and social scientists, quality control personnel, auditors, accountants, and others. The book avoids the need for approximations especially in those cases where many approximations are known to perform poorly. This includes cases where the sample size is small and those cases where certain attributes are rare within the study population. The supporting development and theory of the exact results, provided in the table, are presented in an elementary manner making the book readily useful to a wide audience. While the problem addressed in this book is a common one, the exact solution is not commonly used by many, including statisticians, perhaps because of the involved combinatorics and the required computing. This book removes the need to compute these confidence bounds when sampling from small universes. This book will no doubt serve as a catalyst for research into other exact results and their applications for more complex sampling designs. TOC:Contents: Introduction.- The Applications.- The Development and Theory.- The Table of Lower and Upper Confidence Bounds.- Appendix. A SAS Macro for Generating Exact One-Sided Lower and Upper Confidence Bounds for a for Stated N, n, a, and 1-l.- References.- Index.

Inhalt

1. Introduction.- 2. The Applications.- 2.1. Application I. Exact 100(1-?)% One-Sided Upper and Lower Confidence Bounds for A Under Simple Random Sampling.- 2.2. Application II. Exact 100(1-?)% Two-Sided Confidence Bounds for A Under Simple Random Sampling.- 2.3. Application III. Conservative Confidence Bounds for A Under Simple Random Sampling when N0 Is Not in the Table, but N0 Is Between Two Other Values of N That Are.- 2.2 Application III.1. When a Particular Value n0 is Not in the Table.- 2.4. Application IV. Exact One- and Two-Sided ? Level Tests of Hypotheses Under Simple Random Sampling.- 2.5. Application V. Sample Size Determination Under Simple Random Sampling.- 2.6. Application VI. The Analogous Exact Inferences and Procedures of Applications I, II, III, IV, and V Can All Be Performed for P, the Universe (Population) Proportion, Under Simple Random Sampling.- 2.7. Application VII. Conservative Confidence Bounds Under Stratified Random Sampling with Four or Less Strata.- 2.8. Application VIII. Conservative Comparison of Two Universes.- 3. The Development and Theory.- 3.1. Exact Hypothesis Testing for a Finite Universe.- 3.2. Exact Confidence Interval Estimation for a Finite Universe.- 3.3. Some Additional Results On One-Sided Confidence Bounds.- 4. The Table of Lower and Upper Confidence Bounds.- 4.1. N = 2(1)50.- 4.2. N = 52(2)100.- 4.3. N = 105(5)200.- 4.4. N = 210(10)500.- 4.5. N = 600(100)1000.- 4.6. N = 1100(100)2000.- References.