R by Example / Edition 1

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Overview

R by Example is an example-based introduction to the statistical computing environment that does not assume any previous familiarity with R or other software packages. R functions are presented in the context of interesting applications with real data.

The purpose of this book is to illustrate a range of statistical and probability computations using R for people who are learning, teaching, or using statistics. Specifically, this book is written for users who have covered at least the equivalent of (or are currently studying) undergraduate level calculus-based courses in statistics. These users are learning or applying exploratory and inferential methods for analyzing data and this book is intended to be a useful resource for learning how to implement these procedures in R.

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Editorial Reviews

From the Publisher

From the reviews:

“This book is an example-driven introduction to both R as a programming environment and widely used statistical concepts such as regression, ANOVA, Bayesian modeling, and randomization and simulation experiments. … The book is an excellent addition to the lecture notes for undergraduate and master students as well as a reliable source of examples for lecturers. The exercises presented in parallel with the theoretical aspects guide the reader in an efficient way through the statistical methods.” (Irina Ioana Mohorianu, zbMATH, Vol. 1275, 2014)

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Product Details

  • ISBN-13: 9781461413646
  • Publisher: Springer New York
  • Publication date: 11/23/2011
  • Series: Use R! Series
  • Edition description: 2012
  • Edition number: 1
  • Pages: 359
  • Product dimensions: 6.30 (w) x 9.20 (h) x 1.10 (d)

Meet the Author

Maria Rizzo is Associate Professor in the Department of Mathematics and Statistics at Bowling Green State University.

Jim Albert is Professor of Mathematics and Statistics at Bowling Green State University.

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Table of Contents

Introduction.- Quantitative Data.- Categorical Data.- Presentation Graphics.- Exploratotry Data Analysis.- Basic Inference Models.- Regression.- Analysis of Variance I.- Analysis of Variance II.- Randomiczation tests.- Simulation Experiments.- Bayesian Modeling.- Monte Carlo Methods.
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