Mathematical and statistical software

This volume conveys some of the surprises, puzzles and success stories in high-dimensional and complex data analysis and related fields.

This textbook presents a wide range of tools for a course in mathematical optimization for upper undergraduate and graduate students in mathematics, engineering, computer science, and other applied sciences.

Get started with an accelerated introduction to the R ecosystem, programming language, and tools including R script and RStudio.

This book explores missing data techniques and provides a detailed and easy-to-read introduction to multiple imputation, covering the theoretical aspects of the topic and offering hands-on help with the implementation.

Newly revised to specifically address Microsoft Excel 2019, this book shows the capabilities of Excel in teaching engineering statistics effectively.

The book provides a description of the process of health economic evaluation and modelling for cost-effectiveness analysis, particularly from the perspective of a Bayesian statistical approach.

This book describes why conventional methods fall short to solve the comparability problem and introduces three successive innovations to overcome these shortcomings.

This book explores inductive inference using the minimum message length (MML) principle, a Bayesian method which is a realisation of Ockham's Razor based on information theory.

This textbook on computational statistics presents tools and concepts of univariate and multivariate statistical data analysis with a strong focus on applications and implementations in the statistical software R.

This book provides a general introduction to Sequential Monte Carlo (SMC) methods, also known as particle filters.

This book provides an introduction to quantitative marketing with Python.

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This book provides hands-on guidance for researchers and practitioners in criminal justice and criminology to perform statistical analyses and data visualization in the free and open-source software R.

The main focus of this book is on presenting advances in fuzzy statistics, and on proposing a methodology for testing hypotheses in the fuzzy environment based on the estimation of fuzzy confidence intervals, a context in which not only the data but also the hypotheses are considered to be fuzzy.

Can artificial intelligence learn mathematics?

Dieses Buch ist eine leicht verständliche Einführung in die Statistik, die typische Schlussweisen der Mathematischen Statistik exemplarisch erläutert: Warum kann aus den Ergebnissen einer Stichprobenuntersuchung auf die Gesamtheit geschlossen werden?

This book offers a systematic and rigorous treatment of continuous-time Markov decision processes, covering both theory and possible applications to queueing systems, epidemiology, finance, and other fields.

This book enables readers who may not be familiar with matrices to understand a variety of multivariate analysis procedures in matrix forms.

This contributed book focuses on major aspects of statistical quality control, shares insights into important new developments in the field, and adapts established statistical quality control methods for use in e.

This book discusses the development of the Rosenbrock-Wanner methods from the origins of the idea to current research with the stable and efficient numerical solution and differential-algebraic systems of equations, still in focus.

This book presents recent results in finite mixtures of skewed distributions to prepare readers to undertake mixture models using scale mixtures of skew normal distributions (SMSN).

This essential textbook presents a concise introduction to the fundamental principles of software engineering, together with practical guidance on how to apply the theory in a real-world, industrial environment.

Recent advances in the understanding of star formation and evolution have been impressive and aspects of that knowledge are explored in this volume.

MATLAB and Simulink Crash Course for Engineers is a reader-friendly introductory guide to the features, functions, and applications of MATLAB and Simulink.

This advanced textbook for business statistics teaches, statistical analyses and research methods utilizing business case studies and financial data, with the applications of Excel VBA, Python and R.

This book provides a friendly introduction to the paradigm and proposes a broad panorama of killing applications of the Infinity Computer in optimization: radically new numerical algorithms, great theoretical insights, efficient software implementations, and interesting practical case studies.

The 49Th Scientific meeting of the Italian Statistical Society was held in June 2018 in Palermo, with more than 450 attendants.

Now in its second edition, this introductory statistics textbook conveys the essential concepts and tools needed to develop and nurture statistical thinking.

R for Quantitative Chemistry is an exploration of how the R language can be applied to a wide variety of problems in what is typically termed "e;Quantitative Chemistry"e; or sometimes "e;Analytical Chemistry"e;.

Applied Linear Regression for Business Analytics with R introduces regression analysis to business students using the R programming language with a focus on illustrating and solving real-time, topical problems.

This book introduces the reader to data science using R and the tidyverse.

This book introduces the reader to data science using R and the tidyverse.

This book offers an introduction to applied statistics through data analysis, integrating statistical computing methods.

This book offers an introduction to applied statistics through data analysis, integrating statistical computing methods.

This book provides a reference for people working in the design, development, and manufacturing of medical devices.

Applied Linear Regression for Business Analytics with R introduces regression analysis to business students using the R programming language with a focus on illustrating and solving real-time, topical problems.

This new textbook in signals and systems provides a pedagogically rich approach to what can commonly be a mathematically dry subject.

System Assurances: Modeling and Management updates on system assurance and performance methods using advanced analytics and understanding of software reliability growth modeling from today's debugging team's point-of-view, along with information on preventive and predictive maintenance and the efficient use of testing resources.

The SPSS Survival Manual throws a lifeline to students and researchers grappling with this powerful data analysis software.

This handy reference book detailing the intricacies of R covers version 4.

This book of peer-reviewed contributions presents the latest findings in classification, statistical learning, data analysis and related areas, including supervised and unsupervised classification, clustering, statistical analysis of mixed-type data, big data analysis, statistical modeling, graphical models and social networks.

This brief addresses the estimation of quantile regression models from a practical perspective, which will support researchers who need to use conditional quantile regression to measure economic relationships among a set of variables.

This book presents the state of the art on numerical semigroups and related subjects, offering different perspectives on research in the field and including results and examples that are very difficult to find in a structured exposition elsewhere.

The peer-reviewed contributions gathered in this book address methods, software and applications of statistics and data science in the social sciences.

This book offers a modern and accessible introduction to Statistical Inference, the science of inferring key information from data.

This volume presents a selection of research papers on various topics at the interface of statistics and computer science.

Now in its second edition, Text Analysis with R provides a practical introduction to computational text analysis using the open source programming language R.

This book focuses on recent advances, approaches, theories and applications related to mixture models.