Concise presentation Emphasis on measurement issues critical to quality assurance Coverage that gives insight to correct implementation of quality improvement methods Online access to robust supporting materials, including R code for examples in the text and two sets of slides: one full set for lecture presentation and another with audio from a long-running and highly successful junior-level university course Includes supplementary material: sn.pub/extras Request lecturer material: sn.pub/lecturer-material

Autorentext
Stephen Vardeman is Professor of Statistics and Industrial Engineering at Iowa State University. He is a Fellow of the American Statistical Association and an (elected) Ordinary Member of the International Statistical Institute. His interests include physical science and engineering applications of statistics, statistics and metrology, quality assurance, business applications of statistics, modern ("big") data analytics, statistical machine learning, directional data analysis, reliability, statistics education, and the development of new statistical theory and methods. He has published textbooks in both quality assurance and engineering statistics and recently developed effective new graduate-level coursework in modern statistical machine learning.

J. Marcus Jobe is Professor of Information Systems and Analytics in the Farmer School of Business at Miami University (Ohio). His current research interests focus on measurement quality, multivariate process monitoring, pattern recognition, and oil exploration applications. Professor Jobe has taught and conducted research in Ukraine, and he has won numerous teaching awards at Miami University, including the all-university Outstanding Teaching award and the Richard T. Farmer Teaching Excellence award for senior professors in the school of business. The U.S. Dept. of State twice awarded him the Senior Fulbright Scholar award (1996-1997 and 2005-2006). Marcus Jobe also works as a consultant and has co-authored two statistics texts with Stephen Vardeman.

Inhalt
Introduction.- Statistics and Measurement.- Process Monitoring.- Process Characterization and Capability Analysis.- Experiment Design and Analysis for Process Improvement Part 1: Basics.- Experiment Design and Analysis for Process Improvement Part 2: Advanced Topics.- A Tables.