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.

Autorentext

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.

Klappentext

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.

Inhalt
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.