Download PDF R by Example (Use R!), by Jim Albert, Maria Rizzo
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R by Example (Use R!), by Jim Albert, Maria Rizzo
Download PDF R by Example (Use R!), by Jim Albert, Maria Rizzo
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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.
- Sales Rank: #1302959 in eBooks
- Published on: 2012-01-28
- Released on: 2012-01-28
- Format: Kindle eBook
Review
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) From the Back Cover
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.
About 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.
Most helpful customer reviews
16 of 17 people found the following review helpful.
An Excellent Text on Applied R
By Joseph Hilbe
Albert and Rizzo's book, "R by Example" (Springer, 2012) is one of the finest books I have read in statistics - and I have read
quite a few over the years. It is extremely well written, with many great examples. The authors were also able to explain the
various statistical topics discussed in a very clear manner, anticipating questions or problems that a reader may have.
"R by Example" explains to the reader in detail how to use R for each operation discussed, but at the same time it details
the logic of the statistical methodologies involved.
The text, 372 pages in length, begins by showing readers how to user R for creating functions, vectors, matrices, and so forth,
and for creating and manipulating data frames, installing packages, and the other operations commonly discussed in books on R.
Commencing with Chapter 2, the book explains the use of R in dealing with the following statistical concerns: the statistical
analysis of both bivariate and multivariate quantitative data, handling categorical data, creating a variety of figures and
plots, discussing basic inferential statistics (T-tests, hypothesis testing, nonparametric tests), multiple regression and one
and 2-ways ANOVAs, and the tests appropriate for their evaluation. Again, R is not simply used to examine the above statistical
and graphical methods, but the R language is itself explained in detail within the context of the various statistical/graphical
concerns.
The features that differentiates this book from others lies in Chapters 10-13. Chapter 10 is a brief overview on using
randomization and permutation tests, Chapter 11 provides R code and detailed explanation for examples using simulation, Chapter
12 covers the basics of Bayesian modeling, and Chapter 13 discuses Monte Carlo methods of estimation. The three chapters on
simulation and Bayesian methodology are some 80 pages in length, and provide an extremely clear presentation of the logic of
Bayesian modeling and MCMC sampling. Bayesian estimation is gaining in popularity within the statistical and general research
communities. "R by Example" provides an excellent introduction to the area using R, as well as to applied R programming in
general.
For all of the examples discussed in the book, complete R code is provided and explained in detail. Without a doubt I would
have iiked to have had "R by Example" when first learning how to program in R. I recommend it without reservation.
2 of 2 people found the following review helpful.
I was delighted by the choice of examples
By Myron Hlynka
Since I know something about R, and was interested in the statistical topics in the book,
I jumped into it in Chapter 11. I was delighted by the choice of examples. Even with simple
standard probability examples, the authors added fascinating questions of their own. They gave short
R programs which the reader can run while reading, and comment on surprising results.
I changed some of the commands just to see what effect that would have, which is a great
way to learn. This book presents R the way that I think it should be presented. Highly highly
recommended.
1 of 1 people found the following review helpful.
A true gem!
By espenhb
R by Example is perhaps the most useful book I've ever bought. It covers most topics you're likely to use R to, and shows you the options at hand. It does of course not cover as much as Crawley, but through well-chosen examples it gives insight to a wide array of topics and how the fundamentals work.
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