Presents a detailed exposition of statistical intervals and emphasizes applications in industry. The discussion differentiates at an elementary level among different kinds of statistical intervals and gives instruction with numerous examples and simple math on how to construct such intervals from sample data. This includes confidence intervals to contain a population percentile, confidence intervals on probability of meeting specified threshold value, and prediction intervals to include observation in a future sample. Also has an appendix containing computer subroutines for nonparametric statistical intervals.Content:
Chapter 1 Introduction, Basic Concepts, and Assumptions (pages 1–26):
Chapter 2 Overview of Different Types of Statistical Intervals (pages 27–40):
Chapter 3 Constructing Statistical Intervals Assuming a Normal Distribution Using Simple Tabulations (pages 41–52):
Chapter 4 Methods for Calculating Statistical Intervals for a Normal Distribution (pages 53–74):
Chapter 5 Distribution?Free Statistical Intervals (pages 75–99):
Chapter 6 Statistical Intervals for Proportions and Percentages (Binomial Distribution) (pages 100–114):
Chapter 7 Statistical Intervals for the Number of Occurrences (Poisson Distribution) (pages 115–132):
Chapter 8 Sample Size Requirements for Confidence Intervals on Population Parameters (pages 133–149):
Chapter 9 Sample Size Requirements for Tolerance Intervals, Tolerance Bounds, and Demonstration Tests (pages 150–186):
Chapter 10 Sample Size Requirements for Prediction Intervals (pages 187–200):
Chapter 11 A Review of Other Statistical Intervals (pages 201–234):
Chapter 12 Other Methods for Setting Statistical Intervals (pages 235–244):
Chapter 13 Case Studies (pages 245–283):