Mathematical and statistical software

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

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.

This book provides an introduction to quantitative marketing with Python.

Today, information technology plays a pivotal role in financial control and audit: most financial data is now digitally recorded and dispersed among servers, clouds and networks over which the audited firm has no control.

This textbook offers an easy-to-follow, practical guide to modern data analysis using the programming language R.

This book collects peer-reviewed contributions on modern statistical methods and topics, stemming from the third workshop on Analytical Methods in Statistics, AMISTAT 2019, held in Liberec, Czech Republic, on September 16-19, 2019.

This book presents the latest research on the statistical analysis of functional, high-dimensional and other complex data, addressing methodological and computational aspects, as well as real-world applications.

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

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.

The new edition of this book provides an easily accessible introduction to the statistical analysis of network data using R.

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

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.

Newly revised to specifically address Microsoft Excel 2019, this book is a step-by-step, exercise-driven guide for students and practitioners who need to master Excel to solve practical biological and life science problems.

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

Newly revised to specifically provide demonstration in Excel 2019, this volume shows the capabilities of Microsoft Excel in business statistics.

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.

This book presents a selection of peer-reviewed contributions to the fourth Bayesian Young Statisticians Meeting, BAYSM 2018, held at the University of Warwick on 2-3 July 2018.

This book discusses the modeling and analysis of magnetic resonance imaging (MRI) data acquired from the human brain.

This book depicts a wide range of situations in which there exist finite form representations for the Meijer G and the Fox H functions.

This volume brings together the main results in the field of Bayesian Optimization (BO), focusing on the last ten years and showing how, on the basic framework, new methods have been specialized to solve emerging problems from machine learning, artificial intelligence, and system optimization.

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

This book introduces recently developed mixed finite element methods for large-scale geophysical flows that preserve essential numerical properties for accurate simulations.

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

This volume collects the extended versions of papers presented at the SIS Conference "e;Statistics and Data Science: new challenges, new generations"e;, held in Florence, Italy on June 28-30, 2017.

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 book presents an introduction to linear univariate and multivariate time series analysis, providing brief theoretical insights into each topic, and from the beginning illustrating the theory with software examples.

This textbook will familiarize students in economics and business, as well as practitioners, with the basic principles, techniques, and applications of applied statistics, statistical testing, and multivariate data analysis.

This book presents strategies for analyzing qualitative and mixed methods data with MAXQDA software, and provides guidance on implementing a variety of research methods and approaches, e.

The 2nd edition of R for Marketing Research and Analytics continues to be the best place to learn R for marketing research.

This textbook provides a step-by-step introduction to the class of vine copulas, their statistical inference and applications.

In 2010, the 5th edition of the textbook, "e;Statistics Applied to Clinical Studies"e;, was published by Springer and since then has been widely distributed.

This book provides a tutorial in the use of Altair Compose and Altair Activate, software packages that provide system modeling and simulation facilities.

This textbook provides a step-by-step introduction to the class of vine copulas, their statistical inference and applications.

Statistiques sociales avec IBM SPSSMD est un cahier d’exercices conçu pour accompagner le manuel de William Fox, Statistiques sociales, publié aux Presses de l’Université Laval (Québec) et chez De Boeck (Bruxelles).

This book presents an introduction to linear univariate and multivariate time series analysis, providing brief theoretical insights into each topic, and from the beginning illustrating the theory with software examples.

This book presents advanced research on smart health technologies, focusing on the innovative transformations in diagnosis and treatment planning using medical imaging and data analysed by data science techniques.

This book provides a tutorial in the use of Altair Compose and Altair Activate, software packages that provide system modeling and simulation facilities.

Machine Learning Governance for Managers provides readers with the knowledge to unlock insights from data and leverage AI solutions.

This book presents the theory of matrix algebra for statistical applications, explores various types of matrices encountered in statistics, and covers numerical linear algebra.

This book presents the theory of matrix algebra for statistical applications, explores various types of matrices encountered in statistics, and covers numerical linear algebra.

Learn how to leverage the scientific computing and data analysis capabilities of Python, its standard library, and popular open-source numerical Python packages like NumPy, SymPy, SciPy, matplotlib, and more.

A comprehensive guide to getting well-versed with the mathematical techniques for building modern deep learning architecturesKey FeaturesUnderstand linear algebra, calculus, gradient algorithms, and other concepts essential for training deep neural networksLearn the mathematical concepts needed to understand how deep learning models functionUse deep learning for solving problems related to vision, image, text, and sequence applicationsBook DescriptionMost programmers and data scientists struggle with mathematics, having either overlooked or forgotten core mathematical concepts.

Power and Sample Size in R guides the reader through power and sample size calculations for a wide variety of study outcomes and designs and illustrates their implementation in R software.