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Springer Generalized Linear Models With Examples in R

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Description

Master the fundamentals of applied statistics with Generalized Linear Models With Examples in R from Springer. This textbook provides a comprehensive introduction to GLMs, offering a way to extend multiple linear regression and analysis of variance to non-normally distributed response variables. By learning these methods, you can model diverse data types such as counts, proportions, binary outcomes, and positive quantities. Designed with the student in mind, this book is suitable for both beginning and advanced students of applied statistics. It bridges theory and practice by including real-world data sets and practice problems to ensure you can apply these powerful statistical tools to actual data. Whether you are studying computing or software applications, this text serves as a practical guide for modeling complex data structures.

Key Features

This Springer textbook provides a complete introduction to generalized linear models for applied statistics students.

The content includes real-world data sets to help you apply statistical theories to practical scenarios.

Practice problems are included throughout the book to support both beginning and advanced learners.

Learn to model various data types including counts, proportions, binary outcomes, and positive quantities.

The text extends traditional multiple linear regression and analysis of variance to non-normally distributed variables.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
11 November 2018
Listed Since
04 September 2018

Barcode

No barcode data available

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