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Author : Bernhard F. Arnold
Publisher :
ISBN 13 :
Total Pages : 9 pages
Book Rating : 4.:/5 (246 download)
Download or read book Relative Squared Error Prediction in the Generalized Linear Regression Model written by Bernhard F. Arnold and published by . This book was released on 1999 with total page 9 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Debasis Sengupta
Publisher : World Scientific
ISBN 13 : 9811200424
Total Pages : 773 pages
Book Rating : 4.8/5 (112 download)
Download or read book Linear Models And Regression With R: An Integrated Approach written by Debasis Sengupta and published by World Scientific. This book was released on 2019-07-30 with total page 773 pages. Available in PDF, EPUB and Kindle. Book excerpt: Starting with the basic linear model where the design and covariance matrices are of full rank, this book demonstrates how the same statistical ideas can be used to explore the more general linear model with rank-deficient design and/or covariance matrices. The unified treatment presented here provides a clearer understanding of the general linear model from a statistical perspective, thus avoiding the complex matrix-algebraic arguments that are often used in the rank-deficient case. Elegant geometric arguments are used as needed.The book has a very broad coverage, from illustrative practical examples in Regression and Analysis of Variance alongside their implementation using R, to providing comprehensive theory of the general linear model with 181 worked-out examples, 227 exercises with solutions, 152 exercises without solutions (so that they may be used as assignments in a course), and 320 up-to-date references.This completely updated and new edition of Linear Models: An Integrated Approach includes the following features:
Author : Bernhard F. Arnold
Publisher :
ISBN 13 :
Total Pages : 9 pages
Book Rating : 4.:/5 (247 download)
Download or read book The General Solution of a Matrix Problem Associated with Relative Squared Error Estimation and Prediction in Linear Regression written by Bernhard F. Arnold and published by . This book was released on 2000 with total page 9 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : John Patrick Hoffmann
Publisher : Addison-Wesley Longman
ISBN 13 :
Total Pages : 220 pages
Book Rating : 4.:/5 (321 download)
Download or read book Generalized Linear Models written by John Patrick Hoffmann and published by Addison-Wesley Longman. This book was released on 2004 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: This brief and economical text shows students with relatively little mathematical background how to understand and apply sophisticated linear regression models in their research areas within the social, behavioral, and medical sciences, as well as marketing, and business. Less theoretical than competing texts, Hoffman includes numerous exercises and worked-out examples and sample programs and data sets for three popular statistical software programs: SPSS, SAS, and Stata.
Author : Julian J. Faraway
Publisher : CRC Press
ISBN 13 : 1439887349
Total Pages : 284 pages
Book Rating : 4.4/5 (398 download)
Download or read book Linear Models with R written by Julian J. Faraway and published by CRC Press. This book was released on 2016-04-19 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 response and the predictors. Understanding linear models is crucial to a broader competence in the practice of statistics. Linear Models with R, Second Edition explains how to use linear models
Author : D. A. Harville
Publisher :
ISBN 13 :
Total Pages : 24 pages
Book Rating : 4.:/5 (897 download)
Download or read book Mean Squared Error of Estimation Or Prediction Under a General Linear Model written by D. A. Harville and published by . This book was released on 1989 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Annette J. Dobson
Publisher : CRC Press
ISBN 13 : 1584889519
Total Pages : 316 pages
Book Rating : 4.5/5 (848 download)
Download or read book An Introduction to Generalized Linear Models written by Annette J. Dobson and published by CRC Press. This book was released on 2008-05-12 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: Continuing to emphasize numerical and graphical methods, An Introduction to Generalized Linear Models, Third Edition provides a cohesive framework for statistical modeling. This new edition of a bestseller has been updated with Stata, R, and WinBUGS code as well as three new chapters on Bayesian analysis. Like its predecessor, this edition presents the theoretical background of generalized linear models (GLMs) before focusing on methods for analyzing particular kinds of data. It covers normal, Poisson, and binomial distributions; linear regression models; classical estimation and model fitting methods; and frequentist methods of statistical inference. After forming this foundation, the authors explore multiple linear regression, analysis of variance (ANOVA), logistic regression, log-linear models, survival analysis, multilevel modeling, Bayesian models, and Markov chain Monte Carlo (MCMC) methods. Using popular statistical software programs, this concise and accessible text illustrates practical approaches to estimation, model fitting, and model comparisons. It includes examples and exercises with complete data sets for nearly all the models covered.
Author : Douglas C. Montgomery
Publisher : Wiley-Interscience
ISBN 13 :
Total Pages : 680 pages
Book Rating : 4.F/5 ( download)
Download or read book Introduction to Linear Regression Analysis written by Douglas C. Montgomery and published by Wiley-Interscience. This book was released on 2001-04-16 with total page 680 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive and thoroughly up-to-date look at regression analysis-still the most widely used technique in statistics today As basic to statistics as the Pythagorean theorem is to geometry, regression analysis is a statistical technique for investigating and modeling the relationship between variables. With far-reaching applications in almost every field, regression analysis is used in engineering, the physical and chemical sciences, economics, management, life and biological sciences, and the social sciences. Clearly balancing theory with applications, Introduction to Linear Regression Analysis describes conventional uses of the technique, as well as less common ones, placing linear regression in the practical context of today's mathematical and scientific research. Beginning with a general introduction to regression modeling, including typical applications, the book then outlines a host of technical tools that form the linear regression analytical arsenal, including: basic inference procedures and introductory aspects of model adequacy checking; how transformations and weighted least squares can be used to resolve problems of model inadequacy; how to deal with influential observations; and polynomial regression models and their variations. Succeeding chapters include detailed coverage of: ? Indicator variables, making the connection between regression and analysis-of-variance modelss ? Variable selection and model-building techniques ? The multicollinearity problem, including its sources, harmful effects, diagnostics, and remedial measures ? Robust regression techniques, including M-estimators, Least Median of Squares, and S-estimation ? Generalized linear models The book also includes material on regression models with autocorrelated errors, bootstrapping regression estimates, classification and regression trees, and regression model validation. Topics not usually found in a linear regression textbook, such as nonlinear regression and generalized linear models, yet critical to engineering students and professionals, have also been included. The new critical role of the computer in regression analysis is reflected in the book's expanded discussion of regression diagnostics, where major analytical procedures now available in contemporary software packages, such as SAS, Minitab, and S-Plus, are detailed. The Appendix now includes ample background material on the theory of linear models underlying regression analysis. Data sets from the book, extensive problem solutions, and software hints are available on the ftp site. For other Wiley books by Doug Montgomery, visit our website at www.wiley.com/college/montgomery.
Author : David J. Olive
Publisher : Springer
ISBN 13 : 331955252X
Total Pages : 499 pages
Book Rating : 4.3/5 (195 download)
Download or read book Linear Regression written by David J. Olive and published by Springer. This book was released on 2017-04-18 with total page 499 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text covers both multiple linear regression and some experimental design models. The text uses the response plot to visualize the model and to detect outliers, does not assume that the error distribution has a known parametric distribution, develops prediction intervals that work when the error distribution is unknown, suggests bootstrap hypothesis tests that may be useful for inference after variable selection, and develops prediction regions and large sample theory for the multivariate linear regression model that has m response variables. A relationship between multivariate prediction regions and confidence regions provides a simple way to bootstrap confidence regions. These confidence regions often provide a practical method for testing hypotheses. There is also a chapter on generalized linear models and generalized additive models. There are many R functions to produce response and residual plots, to simulate prediction intervals and hypothesis tests, to detect outliers, and to choose response transformations for multiple linear regression or experimental design models. This text is for graduates and undergraduates with a strong mathematical background. The prerequisites for this text are linear algebra and a calculus based course in statistics.
Author : D. R. Jeske
Publisher :
ISBN 13 :
Total Pages : 12 pages
Book Rating : 4.:/5 (897 download)
Download or read book Mean Squared Error of Estimation and Prediction Under a General Linear Model written by D. R. Jeske and published by . This book was released on 1987 with total page 12 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Arthur S. Goldberger
Publisher :
ISBN 13 :
Total Pages : 10 pages
Book Rating : 4.:/5 (633 download)
Download or read book Stepwise Least Squares written by Arthur S. Goldberger and published by . This book was released on 1961 with total page 10 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Annette J. Dobson
Publisher : CRC Press
ISBN 13 : 1351726218
Total Pages : 354 pages
Book Rating : 4.3/5 (517 download)
Download or read book An Introduction to Generalized Linear Models written by Annette J. Dobson and published by CRC Press. This book was released on 2018-04-17 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: An Introduction to Generalized Linear Models, Fourth Edition provides a cohesive framework for statistical modelling, with an emphasis on numerical and graphical methods. This new edition of a bestseller has been updated with new sections on non-linear associations, strategies for model selection, and a Postface on good statistical practice. Like its predecessor, this edition presents the theoretical background of generalized linear models (GLMs) before focusing on methods for analyzing particular kinds of data. It covers Normal, Poisson, and Binomial distributions; linear regression models; classical estimation and model fitting methods; and frequentist methods of statistical inference. After forming this foundation, the authors explore multiple linear regression, analysis of variance (ANOVA), logistic regression, log-linear models, survival analysis, multilevel modeling, Bayesian models, and Markov chain Monte Carlo (MCMC) methods. Introduces GLMs in a way that enables readers to understand the unifying structure that underpins them Discusses common concepts and principles of advanced GLMs, including nominal and ordinal regression, survival analysis, non-linear associations and longitudinal analysis Connects Bayesian analysis and MCMC methods to fit GLMs Contains numerous examples from business, medicine, engineering, and the social sciences Provides the example code for R, Stata, and WinBUGS to encourage implementation of the methods Offers the data sets and solutions to the exercises online Describes the components of good statistical practice to improve scientific validity and reproducibility of results. Using popular statistical software programs, this concise and accessible text illustrates practical approaches to estimation, model fitting, and model comparisons.
Author : Kevin Kim
Publisher : CRC Press
ISBN 13 : 9781584886341
Total Pages : 576 pages
Book Rating : 4.8/5 (863 download)
Download or read book Univariate and Multivariate General Linear Models written by Kevin Kim and published by CRC Press. This book was released on 2006-10-11 with total page 576 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reviewing the theory of the general linear model (GLM) using a general framework, Univariate and Multivariate General Linear Models: Theory and Applications with SAS, Second Edition presents analyses of simple and complex models, both univariate and multivariate, that employ data sets from a variety of disciplines, such as the social and behavioral sciences. With revised examples that include options available using SAS 9.0, this expanded edition divides theory from applications within each chapter. Following an overview of the GLM, the book introduces unrestricted GLMs to analyze multiple regression and ANOVA designs as well as restricted GLMs to study ANCOVA designs and repeated measurement designs. Extensions of these concepts include GLMs with heteroscedastic errors that encompass weighted least squares regression and categorical data analysis, and multivariate GLMs that cover multivariate regression analysis, MANOVA, MANCOVA, and repeated measurement data analyses. The book also analyzes double multivariate linear, growth curve, seeming unrelated regression (SUR), restricted GMANOVA, and hierarchical linear models. New to the Second Edition Two chapters on finite intersection tests and power analysis that illustrates the experimental GLMPOWER procedure Expanded theory of unrestricted general linear, multivariate general linear, SUR, and restricted GMANOVA models to comprise recent developments Expanded material on missing data to include multiple imputation and the EM algorithm Applications of MI, MIANALYZE, TRANSREG, and CALIS procedures A practical introduction to GLMs, Univariate and Multivariate General Linear Models demonstrates how to fully grasp the generality of GLMs by discussing them within a general framework.
Author : Paul Roback
Publisher : CRC Press
ISBN 13 : 0429527330
Total Pages : 309 pages
Book Rating : 4.4/5 (295 download)
Download or read book Beyond Multiple Linear Regression written by Paul Roback and published by CRC Press. This book was released on 2021-01-05 with total page 309 pages. Available in PDF, EPUB and Kindle. Book excerpt: Beyond Multiple Linear Regression: Applied Generalized Linear Models and Multilevel Models in R is designed for undergraduate students who have successfully completed a multiple linear regression course, helping them develop an expanded modeling toolkit that includes non-normal responses and correlated structure. Even though there is no mathematical prerequisite, the authors still introduce fairly sophisticated topics such as likelihood theory, zero-inflated Poisson, and parametric bootstrapping in an intuitive and applied manner. The case studies and exercises feature real data and real research questions; thus, most of the data in the textbook comes from collaborative research conducted by the authors and their students, or from student projects. Every chapter features a variety of conceptual exercises, guided exercises, and open-ended exercises using real data. After working through this material, students will develop an expanded toolkit and a greater appreciation for the wider world of data and statistical modeling. A solutions manual for all exercises is available to qualified instructors at the book’s website at www.routledge.com, and data sets and Rmd files for all case studies and exercises are available at the authors’ GitHub repo (https://github.com/proback/BeyondMLR)
Author : George H. Dunteman
Publisher : SAGE
ISBN 13 : 9780761920847
Total Pages : 92 pages
Book Rating : 4.9/5 (28 download)
Download or read book An Introduction to Generalized Linear Models written by George H. Dunteman and published by SAGE. This book was released on 2006 with total page 92 pages. Available in PDF, EPUB and Kindle. Book excerpt: Providing a thorough introduction to generalized linear models (GLM), exponential family distribution & maximum likelihood estimation, this book includes discussion on checking model adequacy & description on how to use a popular statistical software programme, SAS, to fit GLM.
Author : Jim Frost
Publisher : Statistics By Jim Publishing
ISBN 13 : 9781735431109
Total Pages : 256 pages
Book Rating : 4.4/5 (311 download)
Download or read book Introduction to Statistics written by Jim Frost and published by Statistics By Jim Publishing. This book was released on 2020-07-21 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn statistics without fear! Build a solid foundation in data analysis. Be confident that you understand what your data are telling you and that you can explain the results to others! I'll help you intuitively understand statistics by using simple language and deemphasizing formulas. This guide starts with an overview of statistics and why it is so important. We proceed to essential statistical skills and knowledge about different types of data, relationships, and distributions. Then we move to using inferential statistics to expand human knowledge, how it fits into the scientific method, and how to design and critique experiments. Learn the fundamentals of statistics. Why is the field of statistics so vital in our data-driven society? Interpret graphs and summary statistics. Find relationships between different types of variables. Understand the properties of data distributions. Use measures of central tendency and variability. Interpret correlations and percentiles. Use probability distributions to calculate probabilities. Learn about the normal distribution and the binomial distributions in depth. Grasp the differences between descriptive and inferential statistics. Use data collection methodologies properly and understand sample size considerations. Critique scientific experiments-whether it's your own or another researcher's.
Author : Judea Pearl
Publisher : John Wiley & Sons
ISBN 13 : 1119186862
Total Pages : 162 pages
Book Rating : 4.1/5 (191 download)
Download or read book Causal Inference in Statistics written by Judea Pearl and published by John Wiley & Sons. This book was released on 2016-01-25 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: CAUSAL INFERENCE IN STATISTICS A Primer Causality is central to the understanding and use of data. Without an understanding of cause–effect relationships, we cannot use data to answer questions as basic as "Does this treatment harm or help patients?" But though hundreds of introductory texts are available on statistical methods of data analysis, until now, no beginner-level book has been written about the exploding arsenal of methods that can tease causal information from data. Causal Inference in Statistics fills that gap. Using simple examples and plain language, the book lays out how to define causal parameters; the assumptions necessary to estimate causal parameters in a variety of situations; how to express those assumptions mathematically; whether those assumptions have testable implications; how to predict the effects of interventions; and how to reason counterfactually. These are the foundational tools that any student of statistics needs to acquire in order to use statistical methods to answer causal questions of interest. This book is accessible to anyone with an interest in interpreting data, from undergraduates, professors, researchers, or to the interested layperson. Examples are drawn from a wide variety of fields, including medicine, public policy, and law; a brief introduction to probability and statistics is provided for the uninitiated; and each chapter comes with study questions to reinforce the readers understanding.
