How does a marble manufacturer know that the color will be consistent throughout the products being made? How can you tell if liquid at the bottom of a container is the same consistency as at the top? How does a pellet manufacturer know if the pellets are consistently the same size? How does a chemical manufacturer know if the percent purity in a sample is representative of the whole batch? These and similar questions are answered in A Primer for Sampling Solids, Liquids, and Gases: Based on the Seven Sampling Errors of Pierre Gy. Author Patricia L. Smith illustrates what to look for in...
How does a marble manufacturer know that the color will be consistent throughout the products being made? How can you tell if liquid at the bottom of ...
Statisticians know that the clean data sets that appear in textbook problems have little to do with real-life industry data. To better prepare their students for all types of statistical careers, academic statisticians now strive to use data sets from real-life statistical problems. This book contains 20 case studies that use actual data sets that have not been simplified for classroom use. Each case study is a collaboration between statisticians from academe and from business, industry, or government. This book is the result of a collaborative workshop of statisticians focusing on...
Statisticians know that the clean data sets that appear in textbook problems have little to do with real-life industry data. To better prepare their s...
Bayesian Nonparametrics via Neural Networks is the first book to focus on neural networks in the context of nonparametric regression and classification, working within the Bayesian paradigm. It discusses neural networks in a statistical context, an approach in contrast to existing books, which tend to treat neural networks as a machine learning algorithm instead of a statistical model. Once this underlying statistical model is recognized, other standard statistical techniques can be applied to improve the model. The Bayesian approach allows better accounting for uncertainty. This book covers...
Bayesian Nonparametrics via Neural Networks is the first book to focus on neural networks in the context of nonparametric regression and classificatio...
Statisticians know that the clean data sets that appear in textbook problems have little to do with real-life industry data. To better prepare their students for all types of statistical careers, academic statisticians now strive to use data sets from real-life statistical problems. This book contains 20 case studies that use actual data sets that have not been simplified for classroom use. Each case study is a collaboration between statisticians from academe and from business, industry, or government. This book is the result of a collaborative workshop of statisticians focusing on...
Statisticians know that the clean data sets that appear in textbook problems have little to do with real-life industry data. To better prepare their s...
Adaptive statistical tests, developed over the last 30 years, are often more powerful than traditional tests of significance, but have not been widely used. To date, discussions of adaptive statistical methods have been scattered across the literature and generally do not include the computer programs necessary to make these adaptive methods a practical alternative to traditional statistical methods. Until recently, there has also not been a general approach to tests of significance and confidence intervals that could easily be applied in practice. Modern adaptive methods are more general...
Adaptive statistical tests, developed over the last 30 years, are often more powerful than traditional tests of significance, but have not been widely...
Engineering reliability concerns failure data analysis, the economics of maintenance policies, and system reliability. This textbook develops the use of probability and statistics in engineering reliability and maintenance problems. The author uses probability models in the analysis of failure data, decisions relative to planned maintenance, and prediction relative to preliminary design. Some of the outstanding features include the analysis of failure data for both continuous and discrete probability from a finite population perspective, probability models derived from engineering...
Engineering reliability concerns failure data analysis, the economics of maintenance policies, and system reliability. This textbook develops the use ...
Matrix analytic methods are popular as modeling tools because they give one the ability to construct and analyze a wide class of queuing models in a unified and algorithmically tractable way. The authors present the basic mathematical ideas and algorithms of the matrix analytic theory in a readable, up-to-date, and comprehensive manner. In the current literature, a mixed bag of techniques is used-some probabilistic, some from linear algebra, and some from transform methods. Here, many new proofs that emphasize the unity of the matrix analytic approach are included.
Matrix analytic methods are popular as modeling tools because they give one the ability to construct and analyze a wide class of queuing models in a u...
