Beyond Multiple Linear Regression

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A01=Julie Legler
A01=Paul Roback
Author_Julie Legler
Author_Paul Roback
Canonical Link
case studies with real data
Category=JMB
Category=PBT
Category=PS
Charter Schools
correlated data
eq_bestseller
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eq_nobargain
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Exponential Family Form
Gamma Poisson Mixture
generalized linear models
hierarchical linear models
Home Team
Likelihood Ratio Test Statistic
likelihood theory
longitudinal data
mixed effects models
Multilevel GLMs
multilevel models
Multiple Linear Regression
Music Performance Anxiety
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Parametric Bootstrap
Parametric Bootstrap Test
Percent Black Residents
Poisson Regression
Poisson Regression Model
Remnant Prairies
Residual Deviance
Score Differential
Shooting Fouls
Unconditional Growth Model
Unconditional Means Model
Variance Std
Visiting Team
zero-inflated Poisson model
Zip Model

Product details

  • ISBN 9780367680442
  • Weight: 660g
  • Dimensions: 156 x 234mm
  • Publication Date: 27 May 2024
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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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)

Authors

Paul Roback is the Kenneth O. Bjork Distinguished Professor of Statistics and Data Science and Julie Legler is Professor Emeritus of Statistics at St. Olaf College in Northfield, MN. Both are Fellows of the American Statistical Association and are founders of the Center for Interdisciplinary Research at St. Olaf. Dr. Roback is the past Chair of the ASA Section on Statistics and Data Science Education, conducts applied research using multilevel modeling, text analysis, and Bayesian methods, and has been a statistical consultant in the pharmaceutical, health care, and food processing industries. Dr. Legler is past Chair of the ASA/MAA Joint Committee on Undergraduate Statistics, is a co-author of Stat2: Modelling with Regression and ANOVA, and was a biostatistician at the National Cancer Institute.

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