Data Analysis Using Regression Models
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Data Analysis Using Regression and Multilevel/Hierarchical Models
Author: Andrew Gelman
language: en
Publisher: Cambridge University Press
Release Date: 2007
This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models.
Statistical Regression Modeling with R
Author: Ding-Geng (Din) Chen
language: en
Publisher: Springer Nature
Release Date: 2021-04-08
This book provides a concise point of reference for the most commonly used regression methods. It begins with linear and nonlinear regression for normally distributed data, logistic regression for binomially distributed data, and Poisson regression and negative-binomial regression for count data. It then progresses to these regression models that work with longitudinal and multi-level data structures. The volume is designed to guide the transition from classical to more advanced regression modeling, as well as to contribute to the rapid development of statistics and data science. With data and computing programs available to facilitate readers' learning experience, Statistical Regression Modeling promotes the applications of R in linear, nonlinear, longitudinal and multi-level regression. All included datasets, as well as the associated R program in packages nlme and lme4 for multi-level regression, are detailed in Appendix A. This book will be valuable in graduate courses on applied regression, as well as for practitioners and researchers in the fields of data science, statistical analytics, public health, and related fields.
Data Analysis Using Regression Models
Designed especially for business and social science students who are familiar with the fundamentals of statistics, this text explores both the theory and practice of regression analysis - proficient in handling the analysis of large data sets. It describes the interaction between data analysis and regression models used to represent the data - to help students learn how to analyze regression data, understand regression models, and how to specify an appropriate model to represent a data set. The main narrative in each chapter stresses application and interpretation of results in applied statistical methods from a user's point of view. Principles are introduced as needed for various applications.