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Extending The Linear Model With R

expanded both in popularity and in the number of packages available. Extending the Linear Model with R: Generalized Linear, Mixed Effects and;

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Simultaneous Inference in Regression

assess part of a multiple linear regression model with the zero function, to compare two regression models, and to evaluate more than two;

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Simultaneous Inference in Regression

assess part of a multiple linear regression model with the zero function, to compare two regression models, and to evaluate more than two;

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An R Companion to Linear Statistical Models

course on regression and analysis of variance. For those who have never used R, the book begins with a self-contained introduction to R that lays;

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An R Companion to Linear Statistical Models

course on regression and analysis of variance. For those who have never used R, the book begins with a self-contained introduction to R that lays;

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Methods And Applications Of Linear Models

approaches to understanding and working with models and theory of linear regression. The Third Edition provides readers with the necessary;

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Extending the Linear Model with R

. Faraway's critically acclaimed Linear Models with R examined regression and analysis of variance, demonstrated the different methods;

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Advanced R Statistical Programming and Data Models

, researchers, or students who are familiar with R and basic statistical techniques such as linear regression and who want to learn how to use R to;

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An Introduction to Generalized Linear Models

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;

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Regression Analysis and Linear Models

, generalized linear model, etc. This book presents the complex subject of regression analysis and linear models, in the most comprehensible and easy to;

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Statistical Regression Modeling with R

This book provides a concise point of reference for the most commonly used regression methods. It begins with linear and nonlinear;

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Statistical Regression Modeling with R

This book provides a concise point of reference for the most commonly used regression methods. It begins with linear and nonlinear;

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A First Course in Linear Model Theory

studying the theory and applications of both univariate and multivariate linear models. In addition to adding R functionality, this second edition;

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Linear Models And Regression With R

Starting with the basic linear model where the design and covariance matrices are of full rank, this book demonstrates how the same;

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Handbook of Regression Methods

provides a broad overview of regression models, diagnostic procedures, and inference procedures, with emphasis on how these methods are applied;

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An Introduction to Generalized Additive Models with R

Generalized Additive Models: An Introduction with R imparts a thorough understanding of the theory and practical applications of GAMs and;

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Advanced Regression Models with SAS and R

Advanced Regression Models with SAS and R exposes the reader to the modern world of regression analysis. The material covered by this book;

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Advanced Regression Models with SAS and R

Advanced Regression Models with SAS and R exposes the reader to the modern world of regression analysis. The material covered by this book;

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Partially Linear Models

In the last ten years, there has been increasing interest and activity in the general area of partially linear regression smoothing;

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Linear Regression Models

*Furnishes a thorough introduction and detailed information about the linear regression model, including how to understand and interpret;

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Linear Regression Models

*Furnishes a thorough introduction and detailed information about the linear regression model, including how to understand and interpret;

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An Introduction to Generalized Linear Models

numerical and graphical methods. This new edition of a bestseller has been updated with new sections on non-linear associations, strategies for;

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An Introduction to Generalized Linear Models

numerical and graphical methods. This new edition of a bestseller has been updated with new sections on non-linear associations, strategies for;

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Analysis of Variance, Design, and Regression

Analysis of Variance, Design, and Regression: Linear Modeling for Unbalanced Data, Second Edition presents linear structures for modeling;

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Analysis of Variance, Design, and Regression

Analysis of Variance, Design, and Regression: Linear Modeling for Unbalanced Data, Second Edition presents linear structures for modeling;

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Introductory Statistics with R

methodology covered includes statistical standard distributions, one- and two-sample tests with continuous data, regression analysis, one-and two-way;

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Spatial Regression Analysis Using Eigenvector Spatial Filtering

sample R codes and a Windows application with illustrative datasets so that readers can replicate the examples in the book and apply the;

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