Please note: In order to keep Hive up to date and provide users with the best features, we are no longer able to fully support Internet Explorer. The site is still available to you, however some sections of the site may appear broken. We would encourage you to move to a more modern browser like Firefox, Edge or Chrome in order to experience the site fully.

Linear Models with R, Hardback Book

Linear Models with R Hardback

Part of the Chapman & Hall/CRC Texts in Statistical Science series

Hardback

Description

A Hands-On Way to Learning Data AnalysisPart of the core of statistics, linear models are used to make predictions and explain the relationship between the response and the predictors.

Understanding linear models is crucial to a broader competence in the practice of statistics.

Linear Models with R, Second Edition explains how to use linear models in physical science, engineering, social science, and business applications.

The book incorporates several improvements that reflect how the world of R has greatly expanded since the publication of the first edition. New to the Second Edition Reorganized material on interpreting linear models, which distinguishes the main applications of prediction and explanation and introduces elementary notions of causality Additional topics, including QR decomposition, splines, additive models, Lasso, multiple imputation, and false discovery ratesExtensive use of the ggplot2 graphics package in addition to base graphics Like its widely praised, best-selling predecessor, this edition combines statistics and R to seamlessly give a coherent exposition of the practice of linear modeling.

The text offers up-to-date insight on essential data analysis topics, from estimation, inference, and prediction to missing data, factorial models, and block designs.

Numerous examples illustrate how to apply the different methods using R.

Information

Other Formats

£86.99

 
Free Home Delivery

on all orders

 
Pick up orders

from local bookshops

Information

Also in the Chapman & Hall/CRC Texts in Statistical Science series  |  View all