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Springer Statistical Regression Modeling with R Book

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Description

Master advanced statistical techniques with Statistical Regression Modeling with R. This Springer publication serves as a concise reference for common regression methods, guiding users from classical approaches to complex data science applications. The content covers linear and nonlinear regression for normally distributed data, alongside logistic regression for binomial distributions. Readers will gain expertise in Poisson and negative-binomial regression for count data. The book specifically addresses the transition to longitudinal and multi-level modeling, making it an essential resource for those working with complex data structures. Designed to support the rapid development of statistics and data science, this volume provides the necessary framework for modeling sophisticated datasets using R.

Key Features

This Springer book provides a concise reference for the most commonly used regression methods in statistics.

The text covers linear and nonlinear regression specifically for normally distributed data.

Learn to apply logistic regression for binomially distributed data and Poisson or negative-binomial regression for count data.

The content guides the transition from classical regression to advanced longitudinal and multi-level modeling.

This volume supports the rapid development of statistics and data science through structured modeling techniques.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
10 April 2022
Listed Since
14 March 2022

Barcode

No barcode data available

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