Beyond Multiple Linear Regression

This book PDF is perfect for those who love Mathematics genre, written by Paul Roback and published by CRC Press which was released on 14 January 2021 with total hardcover pages 436. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Beyond Multiple Linear Regression books below.

Beyond Multiple Linear Regression
Author : Paul Roback
File Size : 53,6 Mb
Publisher : CRC Press
Language : English
Release Date : 14 January 2021
ISBN : 9781439885406
Pages : 436 pages
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Beyond Multiple Linear Regression by Paul Roback Book PDF Summary

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)

Beyond Multiple Linear Regression

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

Get Book
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