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
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