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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)
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.
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.
This timely resource employs actual examples of software process
improvement from the private sector and government, demonstrating
how quality systems, measurement techniques, and performance
evaluations really work-presenting a highly focused methodology for
analyzing an ongoing software development process and establishing
a rational plan for process improvement. Four out of five software
development companies need to overhaul their quality control
procedures and launch process improvement efforts. This book shows
them how to do it Helping developers implement effective quality
control of software systems that can save companies short-term
costs and long-term problems of consumer dissatisfaction, Software
Process Quality focuses on time-to-market pressures that often lead
to quality problems offers unique tools such as the Software
Trouble Assessment Matrix (STAM) and Software Development
Management Dashboard (SDMD) identifies barriers to process
improvement, including poor data collection, insufficient
capitalization, limited training of managers, lack of education
resources, and "remote control" oversight highlights special
statistical tests for software metrics supplies "before and after"
Pareto charts to determine if a process change has resulted in
enhanced quality provides examples of measures and metrics used to
establish quality baselines synthesizes the authors' various tools
and methods by means of a realistic case study of quality
processing and much more Software Process Quality is required
reading for software, quality control, and industrial engineers;
software developers; managers of software development or
maintenance organizations; software project managers; and advanced
undergraduate and graduate students in these disciplines.
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.
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