Analysis of Variance, Design, and Regression

Linear Modeling for Unbalanced Data, Second Edition

Ronald Christensen author

Format:Hardback

Publisher:Taylor & Francis Inc

Published:22nd Dec '15

Currently unavailable, and unfortunately no date known when it will be back

This hardback is available in another edition too:

Analysis of Variance, Design, and Regression cover

Analysis of Variance, Design, and Regression: Linear Modeling for Unbalanced Data, Second Edition presents linear structures for modeling data with an emphasis on how to incorporate specific ideas (hypotheses) about the structure of the data into a linear model for the data. The book carefully analyzes small data sets by using tools that are easily scaled to big data. The tools also apply to small relevant data sets that are extracted from big data.

New to the Second Edition

  • Reorganized to focus on unbalanced data
  • Reworked balanced analyses using methods for unbalanced data
  • Introductions to nonparametric and lasso regression
  • Introductions to general additive and generalized additive models
  • Examination of homologous factors
  • Unbalanced split plot analyses
  • Extensions to generalized linear models
  • R, Minitab®, and SAS code on the author’s website

The text can be used in a variety of courses, including a yearlong graduate course on regression and ANOVA or a data analysis course for upper-division statistics students and graduate students from other fields. It places a strong emphasis on interpreting the range of computer output encountered when dealing with unbalanced data.

Praise for the First Edition:"… written in a clear and lucid style … an excellent candidate for a beginning level graduate textbook on statistical methods … a useful reference for practitioners."
—Zentralblatt für Mathematik

"Being devoted to students mainly, each chapter includes illustrative examples and exercises. The most important thing about this book is that it provides traditional tools for future approaches in the big data domain since, as the author says, the machine learning techniques are directly based on the fundamental statistical methods."
~Marina Gorunescu (Craiova)


Praise for the First Edition:"… written in a clear and lucid style … an excellent candidate for a beginning level graduate textbook on statistical methods … a useful reference for practitioners."
—Zentralblatt für Mathematik

"Being devoted to students mainly, each chapter includes illustrative examples and exercises. The most important thing about this book is that it provides traditional tools for future approaches in the big data domain since, as the author says, the machine learning techniques are directly based on the fundamental statistical methods."
~Marina Gorunescu (Craiova)

ISBN: 9781498730143

Dimensions: unknown

Weight: 1680g

636 pages

2nd edition