Applied Multivariate Statistical Analysis
by Wolfgang K. Hardle, Leopold Simar
Publisher: Springer 2003
Number of pages: 488
The authors' intention is to present multivariate data analysis in a way that is understandable to non-mathematicians and practitioners who are confronted by statistical data analysis. The book has a friendly yet rigorous style. All methods are demonstrated through numerous real examples. Mathematical results are clearly stated.
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by David R. Lilja - University of Minnesota
The book presents one of the fundamental data modeling techniques in an informal tutorial style. Learn how to predict system outputs from measured data using a detailed step-by-step process to develop, train, and test reliable regression models.
by Frederic Barbaresco, Frank Nielsen (eds) - MDPI AG
Contents: Geometric Thermodynamics of Jean-Marie Souriau; Koszul-Vinberg Model of Hessian Information Geometry; Divergence Geometry and Information Geometry; Density of Probability on manifold and metric space; Statistics on Paths and Manifolds; etc.
Statistics is the study of the collection, analysis, interpretation, presentation and organization of data. It deals with all aspects of data including the planning of data collection in terms of the design of surveys and experiments.
by R. Dennis Cook, Sanford Weisberg - Chapman & Hall
In this monograph, we present a detailed account of the residual based methods that we have found to be most useful, and brief summaries of other selected methods. Our emphasis is on graphical methods rather than on formal testing.