http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
"Industrial Statistics is concerned with maintaining and improving the quality of goods and services. It involves a broad range of statistical tools but maintaining and improving quality is its main concern. Variability is inherent in all processes, whether they be manufacturing processes or service processes. This variability must be controlled to create high quality goods and services and must be reduced to improve quality. Industrial Statistics focuses on the use of statistical thinking, i.e., the appreciation of the inherent variability of all processes in order that all possible outcomes can be assessed. It also focuses on developing skills for modeling data and designing experiments that can lead to improvements in performance and reductions in variablity"--
Modern Statistics: A Computer-Based Approach. Statistics and Analytics in Modern Industry -- M.odern Statistics: A Computer-Based Approach. Analyzing Variability: Descriptive Statistics -- Probability Models and Distribution Functions -- Statistical Inference and Bootstrapping -- Variability in Several Dimensions and Regression Models -- Sampling for Estimation of Finite Population Quantities -- Time Series Analysis and Prediction -- Modern Analytic Methods -- Modern Industrial Statistics: Design and Control of Quality and Reliability. The Role of Statistical Methods in Modern Industry and Services -- Basic Tools and Principles of Process Control -- Advanced Methods of Statistical Process Control -- Multivariate Statistical Process Control -- Classical Design and Analysis of Experiments -- Quality by Design -- Computer Experiments -- Reliability Analysis -- Bayesian Reliability Estimation and Prediction -- Sampling Plans for Batch and Sequential Inspection -- List of R Packages -- Solution Manual.
Ron S. Kenett, Shelemyahu Zacks ; with contributions from Daniele Amberti.
Includes bibliographical references and index.