Mathematical and statistical software

Data science unifies statistics, data analysis and machine learning to achieve a better understanding of the masses of data which are produced today, and to improve prediction.

Praise for RiskGrade Your Investments "e;In the same way that the introduction of RiskMetrics raised the level of the discussion (and sometimes debate) regarding market risk measurement and management at large financial institutions, the introduction of RiskGrades and this book represent a major step in the understanding and application of risk measurement and management techniques by individual investors.

Given the improved analytical capabilities of Excel, scientists and engineers everywhere are using it--instead of FORTRAN--to solve problems.

This book is a self-teaching guide to the SPSS for Windows computer package.

Master the math behind CAD, game engines, GIS, and more!

This concise course in data analysis and inference for the mathematically literate builds on survey sampling and designed experiments.

This book highlights the principles of psychological assessment to help researchers and clinicians better develop, evaluate, administer, score, integrate, and interpret psychological assessments.

Physically Unclonable Functions (PUFs) translate unavoidable variations in certain parameters of materials, waves, or devices into random and unique signals.

Spatial Data Science introduces fundamental aspects of spatial data that every data scientist should know before they start working with spatial data.

An accessible introduction to the theory and practice of multivariate analysis for graduates, researchers and professionals dealing with ecological problems.

This book focuses on methods and models in classification and data analysis and presents real-world applications at the interface with data science.

Essentials of Monte Carlo Simulation focuses on the fundamentals of Monte Carlo methods using basic computer simulation techniques.

The goals of this text are to develop the skills and an appreciation for the richness and versatility of modern time series analysis as a tool for analyzing dependent data.

Discover easy-to-follow solutions and techniques to help you to implement applied mathematical concepts such as probability, calculus, and equations using Python's numeric and scientific librariesKey FeaturesCompute complex mathematical problems using programming logic with the help of step-by-step recipesLearn how to utilize Python's libraries for computation, mathematical modeling, and statisticsDiscover simple yet effective techniques for solving mathematical equations and apply them in real-world statisticsBook DescriptionPython, one of the world's most popular programming languages, has a number of powerful packages to help you tackle complex mathematical problems in a simple and efficient way.

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.

This book provides a comprehensive and concrete illustration of time series analysis focusing on the state-space model, which has recently attracted increasing attention in a broad range of 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.

Contingency tables arise in diverse fields, including life sciences, education, social and political sciences, notably market research and opinion surveys.

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.

Modeling spatial and spatio-temporal continuous processes is an important and challenging problem in spatial statistics.

The book covers a wide range of topics, yet essential, in Computational Finance (CF), understood as a mix of Finance, Computational Statistics, and Mathematics of Finance.

Given the explosion of interest in mathematical methods for solving problems in finance and trading, a great deal of research and development is taking place in universities, large brokerage firms, and in the supporting trading software industry.

Query reformulation refers to a process of translating a source query-a request for information in some high-level logic-based language-into a target plan that abides by certain interface restrictions.

Now in its second edition, this edited book presents recent progress and techniques in partial least squares path modeling (PLS-PM), and provides a comprehensive overview of the current state-of-the-art in PLS-PM research.

Maple is a computer algebraic system with a fast-growing number of users in schools, universities, and other institutions.

This book develops the time series univariate models through the Econometric Modeler tool.

This hands-on, no-frills tutorial guide equips new and infrequent users with the SAS skills which are essential to data management.

This book highlights recent advances in natural computing, including biology and its theory, bio-inspired computing, computational aesthetics, computational models and theories, computing with natural media, philosophy of natural computing and educational technology.

Computational complexity is one of the most beautiful fields of modern mathematics, and it is increasingly relevant to other sciences ranging from physics to biology.

Learning a computer language like R can be either frustrating, fun or boring.

This book presents the basic procedures for using SAS Enterprise Guide to analyse statistical data.

Nonlinear physics continues to be an area of dynamic modern research, with applications to physics, engineering, chemistry, mathematics, computer science, biology, medicine and economics.

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 discusses examples in parametric inference with R.

Highlighting the latest advances in nonparametric and semiparametric statistics, this book gathers selected peer-reviewed contributions presented at the 4th Conference of the International Society for Nonparametric Statistics (ISNPS), held in Salerno, Italy, on June 11-15, 2018.

This book is a complete introduction to the power of R for marketing research practitioners.

Program for data analysis using R and learn practical skills to make your work more efficient.

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.

Master Statistical Quality Control using JMP !

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

Its much easier to grasp complex data relationships with a graph than by scanning numbers in a spreadsheet.

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.

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