Statistical Methods: The Geometric Approach (Springer Texts in Statistics)

This book should be considered as a must read for those who want to understand statistics beyond "magical formulas and the rituel of significance tests". Authors manage to provide very concise, clear (and of course visual) depiction of some fundamental statistical methods. I think this book is an easy bridge to be crossed in order to reach the realm of advanced statistical theory. This book is a novel exposition of the traditional workhorses of statistics: analysis of variance and regression. The key feature is that these tools are viewed in their natural mathematical setting, the geometry of finite dimensions. The Authors To introduce ourselves, Dave Saville is a practicing statistician working in agricultural research; Graham Wood is a university lecturer involved in the teaching of statistical methods. Each of us has worked for sixteen years in our current field. Features of the Book People like pictures. One picture can present a set of ideas at a glance, while a series of pictures, each building on the last, can unify a wealth of ideas. Such a series we present in this text by means of a systematic geometric approach to the presentation of the theory of basic statistical methods. This approach fills the void between the traditional extremes of the'cookbook'approach and the'matrix algebra'approach, providing an elementary but at the same time rigorous view of the subject. It combines the virtues of the traditional methods, while avoiding their vices. This book presents a novel exposition of the subjects of analysis of variance and regression. The key feature is that these tools are viewed in their natural mathematical setting, the geometry of finite dimensions. Geometry clarifies the basic statistics and unifies the many aspects of analysis of variance and regression. A novel exposition of the analysis of variance and regression. The key feature here is that these tools are viewed in their natural mathematical setting - the geometry of finite dimensions. This is because geometry clarifies the basic statistics and unifies the many aspects of analysing variance and regression. David J. Saville, Graham R. Wood. Includes Bibliographical References (p. [550]-551) And Index.