A Handbook of Statistical Analyses using R (3rd Ed.)
Auteurs : Hothorn Torsten, Everitt Brian S.
Like the best-selling first two editions, A Handbook of Statistical Analyses using R, Third Edition provides an up-to-date guide to data analysis using the R system for statistical computing. The book explains how to conduct a range of statistical analyses, from simple inference to recursive partitioning to cluster analysis.
New to the Third Edition
- Three new chapters on quantile regression, missing values, and Bayesian inference
- Extra material in the logistic regression chapter that describes a regression model for ordered categorical response variables
- Additional exercises
- More detailed explanations of R code
- New section in each chapter summarizing the results of the analyses
- Updated version of the HSAUR package (HSAUR3), which includes some slides that can be used in introductory statistics courses
Whether you?re a data analyst, scientist, or student, this handbook shows you how to easily use R to effectively evaluate your data. With numerous real-world examples, it emphasizes the practical application and interpretation of results.
An Introduction to R. Data Analysis Using Graphical Displays. Simple Inference. Conditional Inference. Analysis of Variance. Simple and Multiple Linear Regression. Logistic Regression and Generalized Linear Models. Density Estimation. Recursive Partitioning. Scatterplot Smoothers and Additive Models. Survival Analysis. Quantile Regression. Analyzing Longitudinal Data I. Analyzing Longitudinal Data II. Simultaneous Inference and Multiple Comparisons. Missing Values. Meta-Analysis. Bayesian Inference. Principal Component Analysis. Multidimensional Scaling. Cluster Analysis. Bibliography. Index.
Date de parution : 12-2017
15.6x23.4 cm
Disponible chez l'éditeur (délai d'approvisionnement : 14 jours).
Prix indicatif 220,72 €
Ajouter au panierDate de parution : 08-2014
Ouvrage de 421 p.
15.6x23.4 cm
Thèmes d’A Handbook of Statistical Analyses using R :
Mots-clés :
Min 1Q Median 3Q Max; Data Set; statistical analyses; Multiple Linear Regression; R; Head Circumference; graphical displays; Linear Mixed Effect Models; simultaneous inference; Random Intercept Model; longitudinal data; Simple Linear Regression Fit; regression; Cumulative Distribution Function; Brian S; Everitt; Cloud Seeding; analysis of variance; Ab Ilit; Proportional Odds Model; Quantile Regression; Summary Method; NA NA Non; Scatterplot Matrix; Lung Cancer Case Control Study; NA NA; Additive Quantile Regression; Conditional Quantiles; Multiply Imputed Data; Pa Rti; Median Regression Model; Gee Procedure; Scatterplot Smoothers; Missing Values