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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.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Introduction

Abstract
R is a statistical computing environment. It is free (open source) software for statistical computation and graphics [40] and is a computer language designed for typical statistical and graphical applications. The R distribution includes the ability to save and run commands stored in script files, and an integrated editor in the R Graphical User Interface (R-GUI). It is available for most platforms including Unix/Linux, Windows, and Macintosh platforms. Thousands of contributed packages are available, and users are provided tools to make packages.
Jim Albert, Maria Rizzo

Chapter 2. Quantitative Data

Abstract
This chapter covers some basic numerical and graphical summaries of data. Different numerical summaries and graphical displays would be appropriate for different types of data.
Jim Albert, Maria Rizzo

Chapter 3. Categorical data

Abstract
R commands are introduced for organizing, summarizing, and displaying categorical data.
Jim Albert, Maria Rizzo

Chapter 4. Presentation Graphics

Abstract
The purpose of this chapter is to describe methods for adjusting the attributes of the graph and for interacting with the graph that will enable the user to produce a publication-level graphical display. We focus on the methods that we have found useful in our own work.
Jim Albert, Maria Rizzo

Chapter 5. Exploratory Data Analysis

Abstract
Exploratory data analysis is the process by which a person manipulates data with the goal of learning about general patterns or tendencies and finding specific occurrences that deviate from the general patterns. The themes of Revelation, Resistance, Residuals, and Reexpression are illustrated in exploratory work.
Jim Albert, Maria Rizzo

Chapter 6. Basic Inference Methods

Abstract
Basic inferential methods for proportions and means are illustrated using R.
Jim Albert, Maria Rizzo

Chapter 7. Regression

Abstract
Regression is a general statistical method to fit a straight line or other model to data. The objective is to find a model for predicting the dependent variable (response) given one or more independent (predictor) variables.
Jim Albert, Maria Rizzo

Chapter 8. Analysis of Variance I

Abstract
Analysis of Variance (ANOVA) is a statistical procedure for comparing means of two or more populations. As the name suggests, ANOVA is a method for studying differences in means by analysis of the variance components in the model. In earlier chapters we have considered two sample location problems; for example, we compared the means of two groups using a two-sample t-test. Let us now consider a generalization to the multi-sample location problem, where we wish to compare the location parameters of two or more groups. One-way ANOVA handles a special case of this problem, testing for equal group means.
Jim Albert, Maria Rizzo

Chapter 9. Analysis of Variance II

Abstract
Analysis of Variance (ANOVA) is a statistical procedure for comparing means of two or more populations. In Chap. 8 we considered one-way ANOVA models, which help to analyze differences in the mean response corresponding to the levels of a single group variable or factor. In this chapter we consider randomized block designs and two-way ANOVA models. Randomized block designs model the effects of a single group variable or factor while controlling for another source of variation using blocks. Two-way ANOVA models explain differences in the mean response corresponding to the levels of two group variables (factors) and their possible interaction.
Jim Albert, Maria Rizzo

Chapter 10. Randomization Tests

Abstract
If the model assumptions for ANOVA do not hold, then the ANOVA F-test is not necessarily valid for testing the hypothesis of equal means. However, one can compute an ANOVA table and a F statistic; what is in doubt is whether the “F” ratio has a F distribution.
Jim Albert, Maria Rizzo

Chapter 11. Simulation Experiments

Abstract
Simulation provides a straightforward way of approximating probabilities. We illustrate the use of two R functions in simulating some famous probability problems.
Jim Albert, Maria Rizzo

Chapter 12. Bayesian Modeling

Abstract
The basic tenets of Bayesian inference are introduced. This includes the construction of a prior and the use of the posterior distribution to perform inferences. Simulation is helpful in summarizing posterior distributions and Markov chain Monte Carlo algorithms are useful in simulating from posterior distributions of non-familiar forms.
Jim Albert, Maria Rizzo

Chapter 13. Monte Carlo Methods

Abstract
A general problem in probability and statistical applications is the computation of an expectation of a random variable. We illustrate the use of R to compute some expectations by the Monte Carlo method. These computations are helpful in comparing sampling properties of point estimators or evaluating probabilities of coverage of interval estimators. We illustrate the use of Markov chain Monte Carlo (MCMC) methods in simulating from sampling distributions.
Jim Albert, Maria Rizzo

Backmatter

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