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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 essential regression methods used in modern data science. The text covers linear and nonlinear regression for normally distributed data, alongside logistic regression for binomial distributions. It also provides detailed instruction on Poisson and negative-binomial regression for count data. Designed to facilitate the transition from classical modeling to advanced applications, this volume focuses on longitudinal and multi-level data structures. It is a practical resource for those working in statistics and biostatistics, helping to support the rapid development of data science through structured mathematical guidance.

Key Features

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

Learn to apply linear and nonlinear regression models specifically designed for normally distributed data.

The text covers logistic regression for binomially distributed data and Poisson or negative-binomial regression for count data.

Progress from classical regression modeling to advanced methods for longitudinal and multi-level data structures.

Designed to support the rapid development of statistics and data science through structured technical content.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
09 April 2021
Listed Since
09 December 2020

Barcode

No barcode data available

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