Statistical Methods: The Geometric Approach

  • David J. Saville
  • Graham R. Wood

Part of the Springer Texts in Statistics book series (STS)

Table of contents

  1. Front Matter
    Pages i-xv
  2. Basic Ideas

    1. Front Matter
      Pages 1-1
    2. David J. Saville, Graham R. Wood
      Pages 3-9
    3. David J. Saville, Graham R. Wood
      Pages 10-38
    4. David J. Saville, Graham R. Wood
      Pages 39-54
    5. David J. Saville, Graham R. Wood
      Pages 55-64
  3. Introduction to Analysis of Variance

    1. Front Matter
      Pages 65-65
    2. David J. Saville, Graham R. Wood
      Pages 67-96
    3. David J. Saville, Graham R. Wood
      Pages 97-132
    4. David J. Saville, Graham R. Wood
      Pages 133-151
  4. Orthogonal Contrasts

    1. Front Matter
      Pages 153-153
    2. David J. Saville, Graham R. Wood
      Pages 155-186
    3. David J. Saville, Graham R. Wood
      Pages 187-223
    4. David J. Saville, Graham R. Wood
      Pages 224-270
    5. David J. Saville, Graham R. Wood
      Pages 271-295
  5. Introducing Blocking

    1. Front Matter
      Pages 297-297
    2. David J. Saville, Graham R. Wood
      Pages 299-339
    3. David J. Saville, Graham R. Wood
      Pages 340-353
    4. David J. Saville, Graham R. Wood
      Pages 354-379
  6. Fundamentals of Regression

    1. Front Matter
      Pages 381-381

About this book

Introduction

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.

Keywords

Mathematica Variance analysis of variance correlation statistics

Authors and affiliations

  • David J. Saville
    • 1
  • Graham R. Wood
    • 2
  1. 1.AgResearchBiometrics UnitLincolnNew Zealand
  2. 2.Department of MathematicsUniversity of CanterburyChristchurchNew Zealand

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4612-0971-3
  • Copyright Information Springer Science+Business Media New York 1991
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4612-6965-6
  • Online ISBN 978-1-4612-0971-3
  • Series Print ISSN 1431-875X
  • About this book