Linear Models: Searle/Linear

This 1971 classic on linear models is once again available--as a Wiley Classics Library Edition. It features material that can be understood by any statistician who understands matrix algebra and basic statistical methods.Content: Chapter 1 Generalized Inverse Matrices (pages 1–30): Chapter 2 Distributions and Quadratic Forms (pages 31–74): Chapter 3 Regression, or The Full Rank Model (pages 75–134): Chapter 4 Introducing Linear Models: Regression on Dummy Variables (pages 135–163): Chapter 5 Models Not of Full Rank (pages 164–225): Chapter 6 Two Elementary Models (pages 226–260): Chapter 7 The 2?Way Crossed Classification (pages 261–331): Chapter 8 Some Other Analyses (pages 332–375): Chapter 9 Introduction to Variance Components (pages 376–420): Chapter 10 Methods of Estimating Variance Components from Unbalanced Data (pages 421–472): Chapter 11 Variance Component Estimation from Unbalanced Data: Formulae (pages 473–514): Generalized Inverse Matrices -- Distributions And Quadratic Forms -- Regression, Or The Full Rank Model -- Introducing Linear Models: Regression On Dummy Variables -- Models Not Of Full Rank -- Two Elementary Models -- The 2-way Crossed Classification -- Some Other Analyses -- Introduction To Variance Components -- Methods Of Estimating Variance Components From Unbalanced Data -- Variance Component Estimation From Unbalanced Data: Formulae. [by] S. R. Searle. Includes Bibliographical References (pages 515-522) And Index. Generalized inverse matrices; Distributions and quadratic forms; Regression, or the full rank model; Introducing linear models: regression on dummy variables; Models not of full rank; Two elementary models; The 2-way crossed classification; Some other analysis; Introduction to variance components; Methods of estimating variance components from unbalanced data; Variance component estimation from unbalanced data: formulac; Linear models This 1971 classic on linear models is once again available -- as a Wiley Classics Library Edition. It features material that can be understood by any statistician who understands matrix algebra and basic statistical methods."Exercises at the end of each chapter allow readers to understand the logic of establishing the techniques rather than the details of the results themselves.

this 1971 Classic On Linear Models Is Once Again Available—as A Wiley Classics Library Edition. It Features Material That Can Be Understood By Any Statistician Who Understands Matrix Algebra And Basic Statistical Methods.

Describes general procedures of estimation and hypothesis testing for linear statistical models. Shows their application for unbalanced data to certain specific models that often arise in research and survey work The application of generalized inverse matrices of linear statistical models is of relatively recent occurrence.