Kategoria BISAC: Mathematics >> Probability & Statistics - Multivariate Analysis

Drawing on the author s 45 years of experience in multivariate analysis, Correspondence Analysis in Practice, Third Edition, shows how the versatile method of correspondence analysis (CA) can be used for data visualization in a wide variety of situations. CA and its variants, subset CA, multiple CA and joint CA, translate two-way and multi-way tables into more readable graphical forms ideal for applications in the social, environmental and health sciences, as well as marketing, economics, linguistics, archaeology, and more.

Michael Greenacre is Professor of Statistics...

Data where for each entity and variable a distribution is given is called distributional data. In the era of "Big Data," this type of data is becoming more and more prevalent. This edited book presents a synthesis of research in this area over the last twenty years or so. It has been carefully edited to ensure it is consistent with respect to style, level, notation, etc. Each chapter includes real data examples to illustrate the topics and software where appropriate.

This book describes the various methods used for exploratory data analysis with an emphasis on MATLAB implementation. It covers approaches for visualizing data, data tours and animations, clustering (or unsupervised learning), dimensionality reduction, and more. A set of graphical user interfaces (GUIs) allows the users to apply the ideas to their own data. This book is suitable for advanced undergraduates and graduates.

This book is designed primarily for upper level undergraduate and graduate level students taking a course in multilevel modelling and/or statistical modelling with a large multilevel modelling component. The focus is on presenting the theory and practice of major multilevel modelling techniques in a variety of contexts, using Mplus as the software tool, and demonstrating the various functions available for these analyses in Mplus, which is widely used by researchers in various fields, including most of the social sciences. In particular, Mplus offers users a wide array of tools for latent...

Full of real-world case studies and practical advice, Exploratory Multivariate Analysis by Example Using R, Second Edition focuses on four fundamental methods of multivariate exploratory data analysis that are most suitable for applications. It covers principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, and hierarchical cluster analysis.

The authors take a geometric point of view that provides a unified vision for exploring multivariate data tables. Within...

Introductory Statistics, Fourth Edition, reviews statistical concepts and techniques in a manner that will teach students not only how and when to utilize the statistical procedures developed, but also how to understand why these procedures should be used. The text's main merits are the clarity of presentation, contemporary examples and applications from diverse areas, an explanation of intuition, and the ideas behind the statistical methods.

Concepts are motivated, illustrated, and explained in a way that attempts to increase one's intuition. To quote from the preface, it...

After emphasizing the summarization of data in the first several chapters, the authors focus on regression analysis. This chapter provides a link between the two...