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This textbook on computational statistics presents tools and concepts of univariate and multivariate statistical data analysis with a strong focus on applications and implementations in the statistical software R.

This textbook presents methods and techniques for time series analysis and forecasting and shows how to use Python to implement them and solve data science problems. It covers not only common statistical approaches and time series models, including ARMA, SARIMA, VAR, GARCH and state space and Markov switching models for (non)stationary, multivariate and financial time series, but also modern machine learning procedures and challenges for time series forecasting. Providing an organic combination of the principles of time series analysis and Python programming, it enables the reader to study methods and techniques and practice writing and running Python code at the same time. Its data-driven approach to analyzing and modeling time series data helps new learners to visualize and interpret both the raw data and its computed results. Primarily intended for students of statistics, economics and data science with an undergraduate knowledge of probability and statistics, the book will equally appeal to industry professionals in the fields of artificial intelligence and data science, and anyone interested in using Python to solve time series problems.

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.

An overview of the theory and application of linear and nonlinear mixed-effects models in the analysis of grouped data, such as longitudinal data, repeated measures, and multilevel data.

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

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

This text explores developments and solutions for many practical problems confronting quantitative methods in financial research and industry. It is a synthesis of scientific contributions on practical implementation and theoretical concepts.

This book shows how to look at ways of visualizing large datasets, whether large in numbers of cases, or large in numbers of variables, or large in both. All ideas are illustrated with displays from analyses of real datasets.

In this book, Graham Wills bridges the gap between the art and the science of visually representing data. He does not simply give rules and advice, but bases these on general principles and provide a clear path between them.

This textbook provides an introduction to the free software Python and its use for statistical data analysis. It covers common statistical tests for continuous, discrete and categorical data, as well as linear regression analysis and topics from survival analysis and Bayesian statistics.

The statistical methodology covered includes statistical standard distributions, one- and two-sample tests with continuous data, regression analysis, one-and two-way analysis of variance, regression analysis, analysis of tabular data, and sample size calculations.

Stata and R are two very flexible data analysis packages. This book details how to extend the power of Stata through the use of R. It steps through more than thirty packages written in both languages, comparing and contrasting their different approaches.

This is the only advanced programming book on R, the enormously successful open-source system based on the S language. It guides the reader through programming with R, beginning with simple interactive use and progressing by gradual stages.

Computational inference uses modern computational power to simulate distributional properties of estimators and test statistics. This book describes computationally intensive statistical methods in a unified presentation.

Lavishly illustrated with both detailed line drawings and clinical photos, this book offers comprehensive coverage of every aspect of the management of intestinal stomas.

Presenting aspects of numerical analysis applicable to statisticians, this volume enables students to craft their own software and to understand both the advantages and challenges of numerical methods. Topics include numerical stability, accurate approximations, computational complexity and more.

Written for statisticians, computer scientists, geographers, researchers, and others interested in visualizing data. This book presents a foundation for producing almost every quantitative graphic found in scientific journals, newspapers, statistical packages, and data visualization systems.