Sample Size Methodology
eBook - PDF

Sample Size Methodology

  1. 135 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

Sample Size Methodology

About this book

One of the most important problems in designing an experiment or a survey is sample size determination and this book presents the currently available methodology. It includes both random sampling from standard probability distributions and from finite populations. Also discussed is sample size determination for estimating parameters in a Bayesian setting by considering the posterior distribution of the parameter and specifying the necessary requirements. The determination of the sample size is considered for ranking and selection problems as well as for the design of clinical trials. Appropriate techniques for attacking the general question of sample size determination in problems of estimation, tests of hypotheses, selection, and clinical trial design are all presented, and will help the reader in formulating an appropriate problem of sample size and in obtaining the solution. The book can be used as a text in a senior-level or a graduate course on sample size methodology.Annotated list of tables in appendixSupplemental problems at the end of book

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Yes, you can access Sample Size Methodology by M. M. Desu in PDF and/or ePUB format, as well as other popular books in Mathematics & Applied Mathematics. We have over one million books available in our catalogue for you to explore.

Information

Table of contents

  1. Front Cover
  2. Sample Size Methodology
  3. Copyright Page
  4. Table of Contents
  5. Preface
  6. Chapter 1. One-Sample Problems
  7. Chapter 2. Two-Sample Problems
  8. Chapter 3. K–Sample Problems
  9. Chapter 4. Power and Sample Size
  10. Chapter 5. Samples from Finite Populations
  11. Chapter 6. Ranking and Selection
  12. Chapter 7. Biomedical Experiments and Clinical Trials
  13. Appendix Guide to Selected Sample Size Tables
  14. Problems and Supplements
  15. References
  16. Index
  17. Statistical Modeling and Decision Science