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Average Customer Rating:

Not for beginners

Customer Rating:

Despite claiming to be a resource for those unfamiliar with statistics or maths, within 2 paragraphs this books fails to keep to this concept. If you have recently completed a science degree and done well in stats this may be the book for you. But unless you know what a "data sets effect size" is, or how to e"stablish confidence intervals" (both terms used on the first page but not explained), you may find this book to be less than helpful. I'm a MPhil student who did well in stats at BSc level but without a scientific maths dictionary I was lost. This book is good at explaining concepts but completely forgets to explain new terms, like parametric, or what the % sign stands for in a data equation (and its not percent). A classic example of a genius thinking he can still communicate with the average person and failing miserably.

Patchy presentation, but good overall

Customer Rating:

It's a long time since I did any statistics, and it wasn't in much depth. My workplace has a lot of time series data stored in a database; I plan on using R to provide automatic anomaly detection, to aid in capacity planning, and business development -- classic data warehouse tasks, in other words. This book is very helpful in some respects. The pragmatic approach described (in particular the sections on model fitting) seems very good. I enjoyed the inclusion of some historical background on the derivation of the statistical methods, and the chatty style didn't grate. Some of the practical hints and tips, eg how not to build a data frame, seemed as if they would be good for complete newcomers to R. The book essentially fulfils its title as an introduction to statistics using R. My particular interest in time series isn't addressed in this book -- I also bought "The Analysis of Time Series: An Introduction". Now the negatives. I generally like "Statistics: An Introduction Using R", so don't take this as advice not to buy it -- I just hope these things are fixed in a later edition. My quibbles are largely stylistic or production related. * Despite the author's assertion that the book assumes no mathematical knowledge from time to time it dives into notation that isn't adequately explained for the complete novice. It seems as if the depth of prior knowledge varies from chapter to chapter; at some points in the book I felt that it was pitched at a much less experienced audience, and at others that it was right over my head. It seems to fall between the two stools of being an introduction to stats in general, and an introduction to stats with R on the other. Assuming no /a priori/ knowledge seems to me to be the safer course, and was certainly what I was looking for. * The Helvetica font for presenting the source code is irritating; in several cases the characters are ambiguous, and it's generally hard to read. Transcripts of output from the software are presented in a fixed-width font; surely it would be natural to do the same for the input? In fact, the typography generally is poor, and is put to shame by the beautiful Dalgaard book "Introductory Statistics with R". * Although examples are apparently available online, some extra information in the book on graphical techniques (eg the code to generate some of the figures) would have been appreciated. * A nitpick: the assumption is made that R is running on the Windows platform (presumably the author's university labs run on this OS). It would be nice to see a short appendix of platform-specfic information, eg about running R on Mac OS X, Solaris, or Linux. The information on data entry, for example, makes the assuption that Microsoft Excel will be the tool of choice -- I plan on pullig information directly out of a database, and would rather see a section on interfacing R to a proper data source, rather than a glorified grid control. * A final nitpick -- in one place "lose" is misspelt as "loose". I physically wince on reading this page. I haven't spotted any other typos though.

An excellent statistics book as well as an R book	Customer Rating:
I've been hoping to find a book that would allow me to recommend R for my undergraduate and postgraduate students. This is undoubtedly it. In short, not only is this a superb reference and introduction to the R software package, it is one of the most concise and clear introductions to statistics itself. I can not recommend any statistics text more highly than this.

The best Stats Book I've ever read

Customer Rating:

This book achieves what I considered impossible - it genuinely explains how to use the magnificent and FREE statistics package R AND teach you statistics at the same time from scratch. Michael Crawley is a born teacher and writer. As I was working through the book, I kept thinking 'How is the man managing it? This is astonishing' Look at most Stats books and try and find a few with ANY reviewers.. This is the best Stats book I've ever read. BUY IT

great for R beginner!	Customer Rating:
This is a book that you learn R as well as statistics, with all examples available very applicable to real life scenarios. I am a first year PhD student in biology who needs to start picking up Maths as well as a program to analysis. This book so far enables me to learn R as well as recap some of the fundamentals in statistics.