Mathematical and statistical software

This volume presents a comprehensive collection of the latest research findings supporting the current and future implementations and applications of computerized multistage testing (MST).

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.

Deriving business value from analytics is a challenging process.

Applied Statistics with Python: Volume I: Introductory Statistics and Regression concentrates on applied and computational aspects of statistics, focusing on conceptual understanding and Python-based calculations.

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

This book teaches readers to integrate data analysis techniques into humanities research practices using the R programming language.

This book provides a concise overview of a variety of techniques for analyzing statistical, scientific, and financial data, using MATLAB(R) to integrate several approaches to data analysis and statistics.

A Strong Practical Focus on Applications and AlgorithmsComputational Statistics Handbook with MATLAB, Third Edition covers today's most commonly used techniques in computational statistics while maintaining the same philosophy and writing style of the bestselling previous editions.

Geocomputation with Python is a comprehensive resource for working with geographic data with the most popular programming language in the world.

Applied Statistics with Python: Volume I: Introductory Statistics and Regression concentrates on applied and computational aspects of statistics, focusing on conceptual understanding and Python-based calculations.

This is the first book that focusses the attention on applying asymmetric multidimensional scaling (MDS) and describes how to apply it in a practical manner.

This is the first book that focusses the attention on applying asymmetric multidimensional scaling (MDS) and describes how to apply it in a practical manner.

Introduction to Regression Methods for Public Health Using R teaches regression methods for continuous, binary, ordinal, and time-to-event outcomes using R as a tool.

This volume presents a comprehensive collection of the latest research findings supporting the current and future implementations and applications of computerized multistage testing (MST).

No detailed description available for "e;Efficient Algorithm Design"e;.

A culmination of the authors' years of extensive research on this topic, Relational Data Clustering: Models, Algorithms, and Applications addresses the fundamentals and applications of relational data clustering.

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

This book provides a concise overview of a variety of techniques for analyzing statistical, scientific, and financial data, using MATLAB(R) to integrate several approaches to data analysis and statistics.

This book covers the basics of numerical methods.

This book covers the basics of numerical methods.

This book provides a comprehensive exploration of generalization in deep learning, focusing on both theoretical foundations and practical strategies.

Data Science: A First Introduction with Python focuses on using the Python programming language in Jupyter notebooks to perform data manipulation and cleaning, create effective visualizations, and extract insights from data using classification, regression, clustering, and inference.

This book studies data, analytics, and intelligence using Boolean structure.