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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 R Statistical Programming and Data Models

multiple examples using R to achieve results, and concludes with a case study. Written by Matt and Joshua F. Wiley, Advanced R Statistical;

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Generalized Additive Models

of exercises, with solutions in an appendix or in the book's R data package gamair, to enable use as a course text or for self-study.;

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Generalized Additive Models

missed. While McCullagh and Nelder's Generalized Linear Models shows how to extend the usual linear methodology to cover analysis of a range;

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Analysis of Variance, Design, and Regression

data with an emphasis on how to incorporate specific ideas (hypotheses) about the structure of the data into a linear model for the data. The;

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Analysis of Variance, Design, and Regression

data with an emphasis on how to incorporate specific ideas (hypotheses) about the structure of the data into a linear model for the data. The;

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Introduction to General and Generalized Linear Models

Bridging the gap between theory and practice for modern statistical model building, Introduction to General and Generalized Linear Models;

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Flexible Regression and Smoothing

Generalized Linear Models (GLMs) and Generalized Additive Models (GAMs) to accommodate large complex datasets, which are increasingly prevalent;

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Flexible Regression and Smoothing

Generalized Linear Models (GLMs) and Generalized Additive Models (GAMs) to accommodate large complex datasets, which are increasingly prevalent;

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Foundations of Linear and Generalized Linear Models

introduction to quasi-likelihood methods that require weaker distributional assumptions, such as generalized estimating equation methods * An overview;

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Generalized Linear Mixed Models

Generalized Linear Mixed Models: Modern Concepts, Methods and Applications presents an introduction to linear modeling using the;

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An Introduction to Generalized Linear Models

Continuing to emphasize numerical and graphical methods, An Introduction to Generalized Linear Models, Third Edition provides a cohesive;

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Prime and Semiprime Rings Endowed With Some Kinds of Mappings

This advanced algebra book deals with derivations, generalized derivations, centralizers and theta-centralizers. It studies prime and;

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A Handbook of Statistical Analyses Using R

relevant statistical background, along with appropriate references. New to the Second Edition New chapters on graphical displays, generalized;

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Data Analysis Using Hierarchical Generalized Linear Models with R

Since their introduction, hierarchical generalized linear models (HGLMs) have proven useful in various fields by allowing random effects;

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Data Analysis Using Hierarchical Generalized Linear Models with R

Since their introduction, hierarchical generalized linear models (HGLMs) have proven useful in various fields by allowing random effects;

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Generalized Linear Models With Examples in R

This textbook presents an introduction to generalized linear models, complete with real-world data sets and practice problems, making it;

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Kernel Mode Decomposition and the Programming of Kernels

/Gaussian processes, generalized Tikhonov regularization, empirical mode decomposition, and Synchrosqueezing, which are all related to and;

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A First Course in Linear Model Theory

. With an innovative approach, the authors introduce to students the mathematical and statistical concepts and tools that form a foundation for;

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Quantitative Economics with R

want to learn R. It will help economics students gain an intuitive appreciation of applied economics and enjoy engaging with the material actively;

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Statistical Models in S

Statistical Models in S extends the S language to fit and analyze a variety of statistical models, including analysis of variance;

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Generalized, Linear, and Mixed Models

An accessible and self-contained introduction to statistical models-now in a modernized new edition Generalized, Linear, and Mixed Models;

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A Computational Approach to Statistical Learning

A Computational Approach to Statistical Learning gives a novel introduction to predictive modeling by focusing on the algorithmic and;

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A Computational Approach to Statistical Learning

A Computational Approach to Statistical Learning gives a novel introduction to predictive modeling by focusing on the algorithmic and;

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Statistics for Linguists

Statistics for Linguists: An Introduction Using R is the first statistics textbook on linear models for linguistics. The book covers simple;

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Statistics for Linguists

Statistics for Linguists: An Introduction Using R is the first statistics textbook on linear models for linguistics. The book covers simple;

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Regression: Models, Methods and Applications

Now in its second edition, this textbook provides an applied and unified introduction to parametric, nonparametric and semiparametric;

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