Linear Models in Statistics
Published on 22. November 1999
Book
Hardback
600 pages
978-0-471-31564-3 (ISBN)
Description
This unique book takes a look at linear models from a matrix perspective. With an emphasis on the theory of regression and analysis of variance, well-known author, Alvin Rencher, carefully develops the theory, lavishly illustrating it with numerically applied examples. He presents the material at an accessible level while incorporating motivational topics not usually present in books of this type.
Reviews / Votes
"...offers a textbook for a one--semester advanced undergraduate or beginning graduate course...includes more material than...one semester..." (SciTech Book News, Vol. 24, No. 4, December 2000) "An excellent book. Highly recommended. Upper--division undergraduate and graduate students; professionals." (Choice, Vol. 38, No. 7, March 2001) "I would recommend the book to anyone as a reference book for the topics covered... numerous exercises with solutions..." (Technometrics, Vol. 42, No. 4, May 2001) "Rencher's textbook is certainly of interest for students and instructors looking for a mathematical introduction to linear statistical models." (Statistics & Decisions, Volume 19, No 2, 2001) "Gives a solid theoretical foundation to standard topics..." (American Mathematical Monthly, November 2001)More details
Series
Edition
1. Auflage
Language
English
Place of publication
New York
United States
Publishing group
John Wiley and Sons Ltd
Target group
College/higher education
Illustrations
Illustrations
Dimensions
Height: 24 cm
Width: 16 cm
Weight
936 gr
ISBN-13
978-0-471-31564-3 (9780471315643)
Schweitzer Classification
Other editions
New editions
Alvin C. Rencher | G. Bruce Schaalje
Linear Models in Statistics
Book
02/2008
2nd Edition
Wiley
€180.50
Shipment within 10-20 days
Person
ALVIN C. RENCHER, PhD, is Professor of Statistics at Brigham Young University and a Fellow of the American Statistical Association. He is the author of Methods of Multivariate Analysis and Multivariate Statistical Inference and Applications, both available from Wiley.
Content
Matrix Algebra. Random Vectors and Matrices. Multivariate Normal Distribution. Distribution of Quadratic Forms in y. Simple Linear Regression. Multiple Regression: Estimation. Multiple Regression: Tests of Hypotheses and Confidence Intervals. Multiple Regression: Model Validation and Diagnostics. Multiple Regression: Random x's. Analysis of Variance Models. One--Way Analysis of Variance: Balanced Case. Two--Way Analysis of Variance: Balanced Case. Analysis of Variance: Unbalanced Data. Analysis of Covariance. Random Effects Models and Mixed Effects Models. Additional Models. Answers and Hints to Selected Problems. Data Sets and SAS Files. Bibliography.