Process Improvement and CMMI (R) for Systems and Software provides a workable approach for achieving cost-effective process improvements for systems and software. Focusing on planning, implementation, and management in system and software processes, it supplies a brief overview of basic strategic planning models and covers fundamental concepts and approaches for system and software measurement, testing, and improvements. The book represents the significant cumulative experience of the authors who were among the first to introduce quality management to the software development processes. It introduces CMMI (R) and various other software and systems process models. It also provides readers with an easy-to-follow methodology for evaluating the status of development and maintenance processes and for determining the return on investment for process improvements. The authors examine beta testing and various testing and usability programs. They highlight examples of useful metrics for monitoring process improvement projects and explain how to establish baselines against which to measure achieved improvements. Divided into four parts, this practical resource covers: Strategy and basics of quality and process improvement Assessment and measurement in systems and software Improvements and testing of systems and software Managing and reporting data The text concludes with a realistic case study that illustrates how the process improvement effort is structured and brings together the methods, tools, and techniques discussed. Spelling out how to lay out a reasoned plan for process improvement, this book supplies readers with concrete action plans for setting up process improvement initiatives that are effective, efficient, and sustainable.

Provides a theoretical foundation as well as practical tools for the analysis of multivariate data, using case studies and MINITAB computer macros to illustrate basic and advanced quality control methods. This work offers an approach to quality control that relies on statistical tolerance regions, and discusses computer graphic analysis highlighting multivariate profile charts.

This innovative textbook presents material for a course on modern statistics that incorporates Python as a pedagogical and practical resource. Drawing on many years of teaching and conducting research in various applied and industrial settings, the authors have carefully tailored the text to provide an ideal balance of theory and practical applications. Numerous examples and case studies are incorporated throughout, and comprehensive Python applications are illustrated in detail. A custom Python package is available for download, allowing students to reproduce these examples and explore others. The first chapters of the text focus on analyzing variability, probability models, and distribution functions. Next, the authors introduce statistical inference and bootstrapping, and variability in several dimensions and regression models. The text then goes on to cover sampling for estimation of finite population quantities and time series analysis and prediction, concluding with two chapters on modern data analytic methods. Each chapter includes exercises, data sets, and applications to supplement learning. Modern Statistics: A Computer-Based Approach with Python is intended for a one- or two-semester advanced undergraduate or graduate course. Because of the foundational nature of the text, it can be combined with any program requiring data analysis in its curriculum, such as courses on data science, industrial statistics, physical and social sciences, and engineering. Researchers, practitioners, and data scientists will also find it to be a useful resource with the numerous applications and case studies that are included. A second, closely related textbook is titled Industrial Statistics: A Computer-Based Approach with Python. It covers topics such as statistical process control, including multivariate methods, the design of experiments, including computer experiments and reliability methods, including Bayesian reliability. These texts can be used independently or for consecutive courses. The mistat Python package can be accessed at https://gedeck.github.io/mistat-code-solutions/ModernStatistics/ "In this book on Modern Statistics, the last two chapters on modern analytic methods contain what is very popular at the moment, especially in Machine Learning, such as classifiers, clustering methods and text analytics. But I also appreciate the previous chapters since I believe that people using machine learning methods should be aware that they rely heavily on statistical ones. I very much appreciate the many worked out cases, based on the longstanding experience of the authors. They are very useful to better understand, and then apply, the methods presented in the book. The use of Python corresponds to the best programming experience nowadays. For all these reasons, I think the book has also a brilliant and impactful future and I commend the authors for that." Professor Fabrizio RuggeriResearch Director at the National Research Council, ItalyPresident of the International Society for Business and Industrial Statistics (ISBIS)Editor-in-Chief of Applied Stochastic Models in Business and Industry (ASMBI)

Process Improvement and CMMI (R) for Systems and Software provides a workable approach for achieving cost-effective process improvements for systems and software. Focusing on planning, implementation, and management in system and software processes, it supplies a brief overview of basic strategic planning models and covers fundamental concepts and approaches for system and software measurement, testing, and improvements. The book represents the significant cumulative experience of the authors who were among the first to introduce quality management to the software development processes. It introduces CMMI (R) and various other software and systems process models. It also provides readers with an easy-to-follow methodology for evaluating the status of development and maintenance processes and for determining the return on investment for process improvements. The authors examine beta testing and various testing and usability programs. They highlight examples of useful metrics for monitoring process improvement projects and explain how to establish baselines against which to measure achieved improvements. Divided into four parts, this practical resource covers: Strategy and basics of quality and process improvement Assessment and measurement in systems and software Improvements and testing of systems and software Managing and reporting data The text concludes with a realistic case study that illustrates how the process improvement effort is structured and brings together the methods, tools, and techniques discussed. Spelling out how to lay out a reasoned plan for process improvement, this book supplies readers with concrete action plans for setting up process improvement initiatives that are effective, efficient, and sustainable.

Fully revised and updated, this book combines a theoretical background with examples and references to R, MINITAB and JMP, enabling practitioners to find state-of-the-art material on both foundation and implementation tools to support their work. Topics addressed include computer-intensive data analysis, acceptance sampling, univariate and multivariate statistical process control, design of experiments, quality by design, and reliability using classical and Bayesian methods. The book can be used for workshops or courses on acceptance sampling, statistical process control, design of experiments, and reliability. Graduate and post-graduate students in the areas of statistical quality and engineering, as well as industrial statisticians, researchers and practitioners in these fields will all benefit from the comprehensive combination of theoretical and practical information provided in this single volume. Modern Industrial Statistics: With applications in R, MINITAB and JMP: * Combines a practical approach with theoretical foundations and computational support. * Provides examples in R using a dedicated package called MISTAT, and also refers to MINITAB and JMP. * Includes exercises at the end of each chapter to aid learning and test knowledge. * Provides over 40 data sets representing real-life case studies. * Is complemented by a comprehensive website providing an introduction to R, and installations of JMP scripts and MINITAB macros, including effective tutorials with introductory material: www.wiley.com/go/modern-industrial-statistics.

Provides a theoretical foundation as well as practical tools for the analysis of multivariate data, using case studies and MINITAB computer macros to illustrate basic and advanced quality control methods. This work offers an approach to quality control that relies on statistical tolerance regions, and discusses computer graphic analysis highlighting multivariate profile charts.