Statistical Regression Modeling with R : Longitudinal and Multi-level Modeling
2021 ed.
Book Details
Format
Paperback / Softback
Book Series
Emerging Topics in Statistics and Biostatistics
ISBN-10
3030675858
ISBN-13
9783030675851
Edition
2021 ed.
Publisher
Springer Nature Switzerland AG
Imprint
Springer Nature Switzerland AG
Country of Manufacture
GB
Country of Publication
GB
Publication Date
Apr 10th, 2022
Print length
228 Pages
Weight
390 grams
Dimensions
15.40 x 23.30 x 2.00 cms
Product Classification:
Probability & statisticsMathematical & statistical software
Ksh 16,200.00
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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.
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.
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