Mathematical and statistical software

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.

Learn how to solve complex differential equations using MATLAB Introduction to Numerical Ordinary and Partial Differential Equations Using MATLAB teaches readers how to numerically solve both ordinary and partial differential equations with ease.

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 authoritative, expanded and updated second edition of Encyclopedia of Machine Learning and Data Mining provides easy access to core information for those seeking entry into any aspect within the broad field of Machine Learning and Data Mining.

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.

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 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.

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.

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 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 includes a wide selection of the papers presented at the 48th Scientific Meeting of the Italian Statistical Society (SIS2016), held in Salerno on 8-10 June 2016.

This volume features selected contributions on a variety of topics related to linear statistical inference.

This book provides insights into important new developments in the area of statistical quality control and critically discusses methods used in on-line and off-line statistical quality control.

This book is a selection of peer-reviewed contributions presented at the third Bayesian Young Statisticians Meeting, BAYSM 2016, Florence, Italy, June 19-21.

This book develops survey data analysis tools in Python, to create and analyze cross-tab tables and data visuals, weight data, perform hypothesis tests, and handle special survey questions such as Check-all-that-Apply.

This book describes and provides many detailed examples of implementing Digital Soil Mapping (DSM) using R.

Now in its second edition, this textbook introduces readers to the IBM SPSS Modeler and guides them through data mining processes and relevant statistical methods.

This advanced textbook explores small area estimation techniques, covers the underlying mathematical and statistical theory and offers hands-on support with their implementation.

This book focuses on the application and development of information geometric methods in the analysis, classification and retrieval of images and signals.

This edited volume on the latest advances in data science covers a wide range of topics in the context of data analysis and classification.

This text covers both multiple linear regression and some experimental design models.

This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science.

This book systematically addresses the design and analysis of efficient techniques for independent random sampling.

In honor of professor and renowned statistician R.

This edited book focuses on the latest developments in classification, statistical learning, data analysis and related areas of data science, including statistical analysis of large datasets, big data analytics, time series clustering, integration of data from different sources, as well as social networks.

This book focuses on recent advances, approaches, theories, and applications related Hidden Markov Models (HMMs).

This book is a result of a workshop, the 8th of the successful TopoInVis workshop series, held in 2019 in Nykoping, Sweden.

This volume presents the latest advances in statistics and data science, including theoretical, methodological and computational developments and practical applications related to classification and clustering, data gathering, exploratory and multivariate data analysis, statistical modeling, and knowledge discovery and seeking.

This book provides a unified view on a new methodology for Machine Translation (MT).

This book introduces the main theoretical findings related to copulas and shows how statistical modeling of multivariate continuous distributions using copulas can be carried out in the R statistical environment with the package copula (among others).

This introductory textbook presents research methods and data analysis tools in non-technical language.

These lecture notes provide a rapid, accessible introduction to Bayesian statistical methods.

This book provides state-of-the-art and interdisciplinary topics on solving matrix eigenvalue problems, particularly by using recent petascale and upcoming post-petascale supercomputers.