Mathematical and statistical software

If you are a researcher or student with experience in multiple linear regression and want to learn about logistic regression, Paul Allison's Logistic Regression Using SAS: Theory and Application, Second Edition, is for you!

Introducing the IBM SPSS Modeler, this book guides readers through data mining processes and presents relevant statistical methods.

The field of financial econometrics has exploded over the last decade.

This book, Statistical Modeling and Computation, provides a unique introduction to modern statistics from both classical and Bayesian perspectives.

Master Statistical Quality Control using JMP !

This edited volume focuses on recent research results in classification, multivariate statistics and machine learning and highlights advances in statistical models for data analysis.

Explore financial data science using SAS.

This volume presents a selection of articles on statistical modeling and simulation, with a focus on different aspects of statistical estimation and testing problems, the design of experiments, reliability and queueing theory, inventory analysis, and the interplay between statistical inference, machine learning methods and related applications.

Addressing a broad range of big data analytics in cross-disciplinary applications, this essential handbook focuses on the statistical prospects offered by recent developments in this field.

This book was written to provide resource materials for teachers to use in their introductory or intermediate statistics class.

The idea of the Grobner basis first appeared in a 1927 paper by F.

Optical scanning holography (OSH) is an emerging area of interest with many potential novel applications, such as 3-D pattern recognition, 3-D microscopy, 3-D cryptography, and 3-D optical remote sensing.

The aim of this book is to provide, ?

This textbook presents the essential tools and core concepts of data science to public officials, policy analysts, and economists among others in order to further their application in the public sector.

A straightforward introduction to the statistical analysis of language data, designed for students with a non-mathematical background.

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

This book collects contributions written by well-known statisticians and econometricians to acknowledge Leopold Simar's far-reaching scientific impact on Statistics and Econometrics throughout his career.

Compositional data are quantitative descriptions of the parts of some whole, conveying exclusively relative information.

Text Analysis with R for Students of Literature is written with students and scholars of literature in mind but will be applicable to other humanists and social scientists wishing to extend their methodological tool kit to include quantitative and computational approaches to the study of text.

This book offers a collection of recent contributions and emerging ideas in the areas of robust statistics presented at the International Conference on Robust Statistics 2015 (ICORS 2015) held in Kolkata during 12-16 January, 2015.

Computers arechanging the way wethink.

This book constitutes the refereed proceedings of the 20th International Conference on Computational Methods in Systems Biology, CMSB 2022, held in Bucharest, Romania, in September 2022.

Looking back at the years that have passed since the realization of the very first electronic, multi-purpose computers, one observes a tremendous growth in hardware and software performance.

This highly anticipated second edition features new chapters and sections, 225 new references, and comprehensive R software.

Sufficient dimension reduction is a rapidly developing research field that has wide applications in regression diagnostics, data visualization, machine learning, genomics, image processing, pattern recognition, and medicine, because they are fields that produce large datasets with a large number of variables.

This focuses on the developing field of building probability models with the power of symbolic algebra systems.

A must have text for risk modelling and portfolio optimization using R.

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.

This book is the third edition of a successful textbook for upper-undergraduate and early graduate students, which offers a solid foundation in probability theory and statistics and their application to physical sciences, engineering, biomedical sciences and related disciplines.

Linear models courses are often presented as either theoretical or applied.

MatLab, Third Edition is the only book that gives a full introduction to programming in MATLAB combined with an explanation of the software's powerful functions, enabling engineers to fully exploit its extensive capabilities in solving engineering problems.

A comprehensive manual on the efficient modeling and analysis of photonic devices for graduate students and researchers in engineering and physics.

This book provides an introduction to multistate event history analysis.

This second edition is an intensively revised and updated version of the book MATLAB(R) and Design Recipes for Earth Sciences.

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

This is the first textbook that allows readers who may be unfamiliar with matrices to understand a variety of multivariate analysis procedures in matrix forms.

Numerical Python by Robert Johansson shows you how to leverage the numerical and mathematical modules in Python and its Standard Library as well as popular open source numerical Python packages like NumPy, FiPy, matplotlib and more to numerically compute solutions and mathematically model applications in a number of areas like big data, cloud computing, financial engineering, business management and more.

A Tour of Data Science: Learn R and Python in Parallel covers the fundamentals of data science, including programming, statistics, optimization, and machine learning in a single short book.

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Mathematica Cookbook helps you master the application's core principles by walking you through real-world problems.

Would you order a multi-course gourmet buffet and just eat salad?

The book focuses on several skew-normal mixed effects models, and systematically explores statistical inference theories, methods, and applications of parameters of interest.

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

This book of peer-reviewed short papers on methodological and applied statistics and demography is the fourth of four volumes from the 52nd Scientific Meeting of the Italian Statistical Society (SIS 2024), held in Bari, Italy, on June 17-20, 2024.

Bei der Modellierung, Analyse und Steuerung komplexer dynamischer Systeme kann nun erstmals ein Programm verwendet werden, das eine Modellierung in der geforderten Genauigkeit und Flexibilität erlaubt: Aufbauend auf den Vorarbeiten von Vester, Forrester u.

Through this book, researchers and students will learn to use R for analysis of large-scale genomic data and how to create routines to automate analytical steps.

The analysis of time series data is an important aspect of data analysis across a wide range of disciplines, including statistics, mathematics, business, engineering, and the natural and social sciences.