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Applied Multivariate Data Analysis

Regression and Experimental Design

An easy to read survey of data analysis, linear regression models and analysis of variance. The extensive development of the linear model includes the use of the linear model approach to analysis of variance provides a strong link to statistical software packages, and is complemented by a thorough overview of theory. It is assumed that the reader has the background equivalent to an introductory book in statistical inference. Can be read easily by those who have had brief exposure to calculus and linear algebra. Intended for first year graduate students in business, social and the biological sciences. Provides the student with the necessary statistics background for a course in research methodology. In addition, undergraduate statistics majors will find this text useful as a survey of linear models and their applications.

Provides the student with the necessary statistics background for a course in research methodology. In addition, undergraduate statistics majors will find this text useful as a survey of linear models and their applications.

Making Sense of Multivariate Data Analysis

An Intuitive Approach

"This is an ideal text for advanced undergraduate and graduate courses across the social sciences. Practitioners who need to refresh their knowledge of MDA will also find this an invaluable resource."--BOOK JACKET.

"This is an ideal text for advanced undergraduate and graduate courses across the social sciences. Practitioners who need to refresh their knowledge of MDA will also find this an invaluable resource."--BOOK JACKET.

Exploratory and Multivariate Data Analysis

With a useful index of notations at the beginning, this book explains and illustrates the theory and application of data analysis methods from univariate to multidimensional and how to learn and use them efficiently. This book is well illustrated and is a useful and well-documented review of the most important data analysis techniques. Key Features * Describes, in detail, exploratory data analysis techniques from the univariate to the multivariate ones * Features a complete description of correspondence analysis and factor analysis techniques as multidimensional statistical data analysis techniques, illustrated with concrete and understandable examples * Includes a modern and up-to-date description of clustering algorithms with many properties which gives a new role of clustering in data analysis techniques

With a useful index of notations at the beginning, this book explains and illustrates the theory and application of data analysis methods from univariate to multidimensional and how to learn and use them efficiently.

Handbook of Univariate and Multivariate Data Analysis and Interpretation with SPSS

Many statistics texts tend to focus more on the theory and mathematics underlying statistical tests than on their applications and interpretation. This can leave readers with little understanding of how to apply statistical tests or how to interpret their findings. While the SPSS statistical software has done much to alleviate the frustrations of s

Many statistics texts tend to focus more on the theory and mathematics underlying statistical tests than on their applications and interpretation.

Multivariate Data Analysis in Sensory and Consumer Science

This book is an outgrowth of research done by Dr. Gamt Dijsterhuis for his doctoral thesis at the University of Leiden. However, there are also contributions by several other authors, as well, including Eeke van der Burg, John Gower, Pieter Punter, Els van den Broek, and Margo Flipsen. This book discusses the use of Multivariate Data Analysis to solve problems in sensory and consumer research. More specifically the focus is on the analysis of the reactions to certain characteristics of food products, which are in the form of scores given to attributes perceived in the food stimuli; the analyses are multivariate; and the senses are mainly the senses of smell and taste. The four main themes covered in the book are: (1) Individual Differences, (2) Measurement Levels; (3) Sensory-Instrumental Relations, and (4) Time-Intensity Data Analysis. The statistical methods discussed include Principle Components Analysis, Generalized Procrustes Analysis, Multidimensional Scaling, Redundancy Analysis, and Canonical Analysis. This book will be a value to all professionals and students working in the sensory studies

This book is an outgrowth of research done by Dr. Gamt Dijsterhuis for his doctoral thesis at the University of Leiden.