Modern Applied Regressions

This book PDF is perfect for those who love Mathematics genre, written by Jun Xu and published by CRC Press which was released on 08 December 2022 with total hardcover pages 298. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Modern Applied Regressions books below.

Modern Applied Regressions
Modern Applied Regressions
Author : Jun Xu
File Size : 47,6 Mb
Publisher : CRC Press
Language : English
Release Date : 08 December 2022
ISBN : 9780429508721
Pages : 298 pages
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Modern Applied Regressions by Jun Xu Book PDF Summary

Modern Applied Regressions creates an intricate and colorful mural with mosaics of categorical and limited response variable (CLRV) models using both Bayesian and Frequentist approaches. Written for graduate students, junior researchers, and quantitative analysts in behavioral, health, and social sciences, this text provides details for doing Bayesian and frequentist data analysis of CLRV models. Each chapter can be read and studied separately with R coding snippets and template interpretation for easy replication. Along with the doing part, the text provides basic and accessible statistical theories behind these models and uses a narrative style to recount their origins and evolution. This book first scaffolds both Bayesian and frequentist paradigms for regression analysis, and then moves onto different types of categorical and limited response variable models, including binary, ordered, multinomial, count, and survival regression. Each of the middle four chapters discusses a major type of CLRV regression that subsumes an array of important variants and extensions. The discussion of all major types usually begins with the history and evolution of the prototypical model, followed by the formulation of basic statistical properties and an elaboration on the doing part of the model and its extension. The doing part typically includes R codes, results, and their interpretation. The last chapter discusses advanced modeling and predictive techniques—multilevel modeling, causal inference and propensity score analysis, and machine learning—that are largely built with the toolkits designed for the CLRV models previously covered. The online resources for this book, including R and Stan codes and supplementary notes, can be accessed at https://sites.google.com/site/socjunxu/home/statistics/modern-applied-regressions.

Modern Applied Regressions

Modern Applied Regressions by Jun Xu

Modern Applied Regressions creates an intricate and colorful mural with mosaics of categorical and limited response variable (CLRV) models using both Bayesian and Frequentist approaches. Written for graduate students, junior researchers, and quantitative analysts in behavioral, health, and social sciences, this text provides details for doing Bayesian and frequentist data

Get Book
An R Companion to Applied Regression

An R Companion to Applied Regression by John Fox,Sanford Weisberg

An R Companion to Applied Regression is a broad introduction to the R statistical computing environment in the context of applied regression analysis. John Fox and Sanford Weisberg provide a step-by-step guide to using the free statistical software R, an emphasis on integrating statistical computing in R with the practice

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Applied Regression Analysis and Generalized Linear Models by John Fox

Combining a modern, data-analytic perspective with a focus on applications in the social sciences, the Third Edition of Applied Regression Analysis and Generalized Linear Models provides in-depth coverage of regression analysis, generalized linear models, and closely related methods, such as bootstrapping and missing data. Updated throughout, this Third Edition includes

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Data Analysis Using Regression and Multilevel Hierarchical Models

Data Analysis Using Regression and Multilevel Hierarchical Models by Andrew Gelman,Jennifer Hill

This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models.

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A Modern Approach to Regression with R

A Modern Approach to Regression with R by Simon Sheather

This book focuses on tools and techniques for building regression models using real-world data and assessing their validity. A key theme throughout the book is that it makes sense to base inferences or conclusions only on valid models. Plots are shown to be an important tool for both building regression

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Applied regression allows social scientists who are not specialists in quantitative techniques to arrive at clear verbal explanations of their numerical results. Provides a lucid discussion of more specialized subjects: analysis of residuals, interaction effects, specification error, multicollinearity, standardized coefficients, and dummy variables.

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A guide to using the power of S-PLUS to perform statistical analyses, providing both an introduction to the program and a course in modern statistical methods. Readers are assumed to have a basic grounding in statistics, thus the book is intended for would-be users, as well as students and researchers

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Modern Applied Statistics with S

Modern Applied Statistics with S by W.N. Venables,B.D. Ripley

A guide to using S environments to perform statistical analyses providing both an introduction to the use of S and a course in modern statistical methods. The emphasis is on presenting practical problems and full analyses of real data sets.

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