Methods and Applications of Linear Models - Info and Reading Options
Regression and the Analysis of Variance
By Ronald R. Hocking
"Methods and Applications of Linear Models" is published by Wiley-Interscience in 1996-06-27 - New York (State), the book is classified in Mathematics genre, it has 776 pages and the language of the book is English.
“Methods and Applications of Linear Models” Metadata:
- Title: ➤ Methods and Applications of Linear Models
- Author: Ronald R. Hocking
- Language: English
- Number of Pages: 776
- Is Family Friendly: Yes - No Mature Content
- Publisher: Wiley-Interscience
- Publish Date: 1996-06-27
- Publish Location: New York (State)
- Genres: Mathematics - bibliography
- Dewey Decimal Classification: 519.5/36
- Library of Congress Classification: QA278.2 .H63 1996
“Methods and Applications of Linear Models” Subjects and Themes:
- Subjects: ➤ Regression analysis - Analysis of variance - Linear models (Statistics) - Linear Models - Regression Analysis - Analysis of Variance - Statistical analysis
Edition Specifications:
- Number of Pages: xxii, 731 p. : ill. ; 24 cm.
Edition Identifiers:
- Google Books ID: hjTvAAAAMAAJ
- Online Computer Library Center (OCLC) ID: 33898038
- Library of Congress Control Number (LCCN): ^^^95048052^
- ISBN-13: 9780471592822
- All ISBNs: 047159282X - 9780471592822
AI-generated Review of “Methods and Applications of Linear Models”:
"Methods and Applications of Linear Models" Table Of Contents:
- 1- 1. Introduction to Linear Models
- 2- 2. The Distribution of Linear and Quadratic Forms
- 3- 3. Estimation and Inference in Simple Linear Models
- 4- 4. Simultaneous Inference: Tests and Confidence Intervals
- 5- 5. Regression on Functions of One Variable
- 6- 6. Transforming the Data and Miscellaneous Topics
- 7- 7. Regression on Functions of Several Variables
- 8- 8. Collinearity in Multiple Linear Regression
- 9- 9. Influential Observations in Multiple Linear Regression
- 10- 10. Polynomial Models and Models with Qualitative Predictors
- 11- 11. Related Topics
- 12- 12. Fixed Effects Models: I. Single
- 13- actor Classification of Means
- 14- 13. Fixed Effects Models: II. Two
- 15- ay Cross
- 16- lassification
- 17- 14. Fixed Effects Models: III. Nested Factors and General Structure
- 18- 15. Mixed Effects Models: I. The AOV with Balanced Data
- 19- 16. Mixed Effects Models: II. The AVE Method
- 20- 17. Mixed Effects Models: III. Unbalanced Data
- 21- Appendix A Mathematical Facts
- 22- Appendix B Statistical Facts.
Snippets and Summary:
Special features include: Data sets available on an ftp site Graphical illustrations of many of the analyses A data-based approach to development and analysis Graphical and numerical diagnostic methods in regression Use of the cell means ...
"Methods and Applications of Linear Models" Description:
Google Books:
A novel approach to linear model analysis Methods and Applications of Linear Models provides a clear and concise summary of the concepts and methodologies of linear models and illustrates the analysis with numerous exercises and real-world examples. Special features include: Data sets available on an ftp site Graphical illustrations of many of the analyses A data-based approach to development and analysis Graphical and numerical diagnostic methods in regression Use of the cell means model for the analysis of variance The introduction of the AVE method for variance component estimation A general approach to the analysis of unbalanced mixed models This novel approach to linear model analysis offers a unified treatment of linear regression and the analysis of variance. The focus is on the appropriate interpretation of results. Carefully chosen examples illustrate the analyses and some of the common sources of confusion in the application of the methods. The treatment of mixed models includes material that has not previously appeared in the literature. For upper-level undergraduate and graduate students of regression and the analysis of variance, this volume provides simple explanations of the basic methodologies. It is also a valuable professional reference for applied statisticians and researchers. Ronald R. Hocking is Professor Emeritus in the Department of Statistics at Texas A&M University. He received his PhD in mathematics and statistics and is a Fellow of the American Statistical Association.
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- Harvard University Library: Location: Baker Library, Harvard Business School, Harvard University - Shelf Numbers: QA278.2 .H63 1996
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