9 of the Best Free R Books - Part 2

4. Using R for Introductory Statistics

The cost of statistical computing software has precluded many universities from installing these valuable computational and analytical tools. R, a powerful open-source software package, was created in response to this issue. It has enjoyed explosive growth since its introduction, owing to its coherence, flexibility, and free availability. While it is a valuable tool for students who are first learning statistics, proper introductory materials are needed for its adoption.

Using R for Introductory Statistics fills this gap in the literature, making the software accessible to the introductory student. The author presents a self-contained treatment of statistical topics and the intricacies of the R software. The pacing is such that students are able to master data manipulation and exploration before diving into more advanced statistical concepts. The book treats exploratory data analysis with more attention than is typical, includes a chapter on simulation, and provides a unified approach to linear models.

This text lays the foundation for further study and development in statistics using R. Appendices cover installation, graphical user interfaces, and teaching with R, as well as information on writing functions and producing graphics. 

Chapters include:

• Univariate Data
• Bivariate Data
• Multivariate Data
• Random Data
• Simulations
• Exploratory Data Analysis
• Confidence Interval Estimation
• Hypothesis Testing
• Two-sample tests
• Chi Square Tests
• Regression Analysis
• Multiple Linear Regression
• Analysis of Variance

This is an ideal text for integrating the study of statistics with a powerful computational tool.

5. An Introduction to R

This tutorial manual provides a comprehensive introduction to R, a software package for statistical computing and graphics.

R supports a wide range of statistical techniques and is easily extensible via user-defined functions. One of R's strengths is the ease with which publication-quality plots can be produced in a wide variety of formats.

Chapters explore:

• Simple manipulations; numbers and vectors
• Objects, their modes and attributes
• Ordered and unordered functions
• Arrays and matrices
• Lists and data frames
• Reading data from files
• Probability distributions
• Grouping, loops and conditional execution
• Writing your own functions
• Statistical models in R
• Graphical procedures
• Packages

6. Practical Regression and Anova in R

Practical Regression and Anova in R is an intermediate text on the practice of regression and analysis of variance. The objective is to learn what methods are available and more importantly, when they should be applied. The book is not an introduction to R.

Chapters cover:

• Estimation
• Inference
• Errors in Predictors
• Generalized Least Squares
• Testing for Lack of Fit
• Diagnostics
• Transformation
• Scale Changes, Principal Components and Collinearity
• Variable Selection
• Statistical Strategy and Model Uncertainty
• Chicago Insurance Redlining - a complete example
• Robust and Registant Regression
• Missing Data
• Analysis of Covariance
• ANOVA