Richly Parameterized Linear Models

Richly Parameterized Linear Models
Author: James S. Hodges
Publisher: CRC Press
Total Pages: 464
Release: 2016-04-19
Genre: Mathematics
ISBN: 1439866848


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A First Step toward a Unified Theory of Richly Parameterized Linear ModelsUsing mixed linear models to analyze data often leads to results that are mysterious, inconvenient, or wrong. Further compounding the problem, statisticians lack a cohesive resource to acquire a systematic, theory-based understanding of models with random effects.Richly Param


Richly Parameterized Linear Models
Language: en
Pages: 464
Authors: James S. Hodges
Categories: Mathematics
Type: BOOK - Published: 2016-04-19 - Publisher: CRC Press

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A First Step toward a Unified Theory of Richly Parameterized Linear ModelsUsing mixed linear models to analyze data often leads to results that are mysterious,
Linear Models with R
Language: en
Pages: 284
Authors: Julian J. Faraway
Categories: Mathematics
Type: BOOK - Published: 2016-04-19 - Publisher: CRC Press

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A Hands-On Way to Learning Data AnalysisPart of the core of statistics, linear models are used to make predictions and explain the relationship between the resp
Advanced Linear Modeling
Language: en
Pages: 618
Authors: Ronald Christensen
Categories: Mathematics
Type: BOOK - Published: 2019-12-20 - Publisher: Springer Nature

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This book introduces several topics related to linear model theory, including: multivariate linear models, discriminant analysis, principal components, factor a
Extending the Linear Model with R
Language: en
Pages: 399
Authors: Julian J. Faraway
Categories: Mathematics
Type: BOOK - Published: 2016-03-23 - Publisher: CRC Press

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Start Analyzing a Wide Range of Problems Since the publication of the bestselling, highly recommended first edition, R has considerably expanded both in popular
Linear Models and the Relevant Distributions and Matrix Algebra
Language: en
Pages: 789
Authors: David A. Harville
Categories: Mathematics
Type: BOOK - Published: 2018-03-22 - Publisher: CRC Press

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Linear Models and the Relevant Distributions and Matrix Algebra provides in-depth and detailed coverage of the use of linear statistical models as a basis for p