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Statistics: An Introduction using R [Kindle Edition]

Michael J. Crawley
4.1 out of 5 stars  See all reviews (23 customer reviews)

Kindle Price: $50.00

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Book Description

Computer software is an essential tool for many statistical modelling and data analysis techniques, aiding in the implementation of large data sets in order to obtain useful results. R is one of the most powerful and flexible statistical software packages available, and enables the user to apply a wide variety of statistical methods ranging from simple regression to generalized linear modelling. Statistics: An Introduction using R is a clear and concise introductory textbook to statistical analysis using this powerful and free software, and follows on from the success of the author's previous best-selling title Statistical Computing.
* Features step-by-step instructions that assume no mathematics, statistics or programming background, helping the non-statistician to fully understand the methodology.
* Uses a series of realistic examples, developing step-wise from the simplest cases, with the emphasis on checking the assumptions (e.g. constancy of variance and normality of errors) and the adequacy of the model chosen to fit the data.
* The emphasis throughout is on estimation of effect sizes and confidence intervals, rather than on hypothesis testing.
* Covers the full range of statistical techniques likely to be need to analyse the data from research projects, including elementary material like t-tests and chi-squared tests, intermediate methods like regression and analysis of variance, and more advanced techniques like generalized linear modelling.
* Includes numerous worked examples and exercises within each chapter.
* Accompanied by a website featuring worked examples, data sets, exercises and solutions:

http://www.imperial.ac.uk/bio/research/crawley/statistics

Statistics: An Introduction using R is the first text to offer such a concise introduction to a broad array of statistical methods, at a level that is elementary enough to appeal to a broad range of disciplines. It is primarily aimed at undergraduate students in medicine, engineering, economics and biology - but will also appeal to postgraduates who have not previously covered this area, or wish to switch to using R.


Editorial Reviews

Review

"I would recommend this book to those who need to teach statistics via the medium of R and those self learners who want to acquire the basic techniques of statistics together with powerful statistical software." (Technometrics, May 2006)

"…will provide you with enhanced statistical insights…and access to a free and powerful computing language." (Clinical Chemistry, May 2006)

"...I know of no better book of its kind..." (Journal of the Royal Statistical Society, Vol 169 (1), January 2006)

"…offers a demanding, non-calculus-based coverage of such standard topics as hypothesis testing, modeling, regression, ANOVA, and count data." (CHOICE, November 2005)

From the Back Cover

Computer software is an essential tool for many statistical modelling and data analysis techniques, aiding in the implementation of large data sets in order to obtain useful results. R is one of the most powerful and flexible statistical software packages available, and enables the user to apply a wide variety of statistical methods ranging from simple regression to generalized linear modelling. Statistics: An Introduction using R is a clear and concise introductory textbook to statistical analysis using this powerful and free software, and follows on from the success of the author’s previous best-selling title Statistical Computing.
  • Features step-by-step instructions that assume no mathematics, statistics or programming background, helping the non-statistician to fully understand the methodology.
  • Uses a series of realistic examples, developing step-wise from the simplest cases, with the emphasis on checking the assumptions (e.g. constancy of variance and normality of errors) and the adequacy of the model chosen to fit the data.
  • The emphasis throughout is on estimation of effect sizes and confidence intervals, rather than on hypothesis testing.
  • Covers the full range of statistical techniques likely to be need to analyse the data from research projects, including elementary material like t-tests and chi-squared tests, intermediate methods like regression and analysis of variance, and more advanced techniques like generalized linear modelling.
  • Includes numerous worked examples and exercises within each chapter.

Statistics: An Introduction using R is the first text to offer such a concise introduction to a broad array of statistical methods, at a level that is elementary enough to appeal to a broad range of disciplines. It is primarily aimed at undergraduate students in medicine, engineering, economics and biology – but will also appeal to postgraduates who have not previously covered this area, or wish to switch to using R.


Product Details

  • File Size: 8062 KB
  • Print Length: 342 pages
  • Publisher: Wiley; 1 edition (December 9, 2011)
  • Sold by: Amazon Digital Services, Inc.
  • Language: English
  • ASIN: B000SGHLWG
  • Text-to-Speech: Enabled
  • X-Ray:
  • Word Wise: Not Enabled
  • Lending: Enabled
  • Amazon Best Sellers Rank: #775,389 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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Customer Reviews

Most Helpful Customer Reviews
69 of 71 people found the following review helpful
3.0 out of 5 stars decent book but uneven April 30, 2007
By kleytos
Format:Paperback|Verified Purchase
This book purports to be an introduction to statistics using R. R has exploded in popularity and today is probably the most powerful system available for doing statistics, having surpassed the older Splus and SAS. Thus you do well to learn R early on as you begin statistics; it well suits the novice and the expert. To make things even better, R is both open source and free with an excellent, supportive online community of many people. The online mailing lists are a treasure trove of valuable resources. There are now several introductory books to R, including one by Verzani, one by Dalgaard, and one by Crawley.

Crawley's book is a _very_ rapid tour through a lot of statistics. There is no real way that a beginner could properly digest the material. Moreover, he often assumes far too much and then assumes far too little. For example in one early chapter he covers the basics of General Linear Models (GLMs), an intermediate to advanced concept. At the beginning of the next chapter, he is explaining basics about the slope of a line! There are a lot of similar examples that left me scratching my head.

There are good pearls in the book that are quite nice, however this book should really be for those with some exposure to statistics.

A better introductory book is "Using R for Introductory Statistics" by John Verzani. That book was more clear and better organized.
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41 of 43 people found the following review helpful
5.0 out of 5 stars More about R than about Statistics January 9, 2007
Format:Paperback|Verified Purchase
The title of this book is a misnomer. It is not an introduction to statistics at all, although it does do a very clear review of courses in descriptive statistics, regression, ANOVA, ANCOVA and GLM. If you don't know statistics, and want to learn, this is not the book for you.

This is, however, a truly excellent book that gets you up to speed very quickly on a wide variety of statistical applications using R as the tool for solution. If you have a reasonable background in statistics and want to use R as a substitute for SAS, SPSS, BMD or other package, this book will teach you how within a week. (Make sure you download the examples from the referenced website.
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24 of 27 people found the following review helpful
5.0 out of 5 stars Excellent introduction to R June 13, 2006
Format:Paperback
Well written and easy to understand. Written by an ecologist for anyone with some statistical background with anova, regression, and more. Great review of basic statistics with code in R and exercises/ data available online at M. Crawley's website. In my opinion, better than Data Analysis and Graphics Using R.
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12 of 13 people found the following review helpful
3.0 out of 5 stars No deception here May 17, 2007
Format:Paperback
This book does a good job of what its designed to do. I would have to agree with another reviewer that finds the topics covered in this book a little too much for a intro statistics book. My intro class certainly didn't cover variable transformation and other more complex topics. I would have to say that I don't feel 100% comfortable navigating through R now, but I have exponentially increased my understanding. I wasn't too impressed with the last few chapters. It seemed as if the coverage of material decreased as the complexity of the statistical tests became more complex. The secion on survival analysis, for example, spans only a few pages. That being said, you do get the R code right in front of you to expose you to how the code needs to be set up. This book also comes with matching chapter lessons that can be downloaded from the authors website. Unfortunately, many of the exercises are nothing more than repeats of the same material in the book. This hurts. I like learning through structured examples...And I prefer more rather than less. So if you are really motivated to learn R by working through some elementary inferential statistics (standard deviation, t-tests, and anovas) then this book can produce results. If your looking for more advanced content (information on examples installing and using packages affiliated with R) than this book doesn't won't meet all of your goals. I would recomend it to someone with little or no knowledge of R and the patience to work through the basics on your own.
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9 of 10 people found the following review helpful
Format:Paperback
If you have already had some experience with statistical methods and are looking for a refresher or a way to quickly pickup the basics of R, this is the place to go. It has a wonderfully conversational tone that is missing from far too many scientifically oriented books, and he brings quite a few insights into the practice of statistics that are more difficult to pickup from the heavily theoretical books.

I would agree with a previous reviewer that there is a bit more space than necessary dedicated to relatively simple concepts, but such minor transgressions are easily overlooked given the overall effectiveness of the book.

I would recommend this book as a refresher/introduction to R, or as a companion book to a college course on statistical methods. The author doesn't cover theory at all (on purpose), so keep in mind this is purely a practical book.

I would have given the book 5 stars if it weren't for a few typos that might confuse beginners or people who have a tendency to read when you're a bit to tired to do so (for example, on the bottom of p59 he says lower bound when he meant upper, nothing you wouldn't catch with a careful re-reading).
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4 of 4 people found the following review helpful
Format:Paperback|Verified Purchase
This is an excellent starting place for using R. I am using a GNU/Linux platform and needed help in moving from SPSS and Stata to a Linux compatible analysis tool. This is a great book for someone with a statistical background or needing a bit of a review. You will gain a better understanding of the theory behind the different types of analysis, and the examples in the book are great. R is very powerful, and the book helps you access that power directly from the command prompt; without a GUI interface. R Commander and RKWARD are fine for simple work, but the real power of R is accessed through the command prompt. This is the book!

I would highly recommend this book as an introduction to a great analysis software that has a bit of a steep learning curve. This will get you back to work and back to publication.
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Most Recent Customer Reviews
5.0 out of 5 stars Five Stars
Great Book.
Published 1 month ago by Michael
4.0 out of 5 stars Four Stars
This is a helpful resource.
Published 7 months ago by George
3.0 out of 5 stars Not impressed or dissatisfied
It's what i ordered but not in quite as good of condition as i thought it would be. It will suffice though.
Published 16 months ago by Mark McCants
5.0 out of 5 stars Great way to learn R
I am a statistician who already uses SAS and SPSS. More of my clients are asking if I can use R. This book is a great tutorial for me, because I can focus on learning R.
Published 16 months ago by Beverly Grunden
5.0 out of 5 stars Great intro to R (not a great intro to statistics)
This book isn't really an intro to statistics, but it is a great introduction to R. I am in an econometrics class that requires R, and the two assigned textbooks are completely... Read more
Published 18 months ago by Jeremy
5.0 out of 5 stars Simply wonderful
The book was just as i expected. It was brand new, in great shape and serving the purpose for which i wanted it. Thank you.
Published 23 months ago by Anthony Cummings
5.0 out of 5 stars This book is Gold
This is simply the best book in statistics I have had the pleasure of reading. The author has cleverly structured the book in such a way to answer common but not well understood... Read more
Published on May 1, 2013 by Pradeep Viv
5.0 out of 5 stars Great book if you already have good understanding of Statistics
If you are serious about learning R, then you should be serious about learning stats and mastering the contents in this book. Read more
Published on January 16, 2013 by Jaewoo Kim
5.0 out of 5 stars Excellent Review/Refresher
I needed a refresher and Prof. Crawley's book turned out to be just what I was looking for (and needed. Read more
Published on December 31, 2012 by K. Casey
4.0 out of 5 stars Learn statistics the easy way.
The author does exactly what the title promises: an introduction to statistics using R. This is by far the clearest introduction to basic stastistical concepts I have read in a... Read more
Published on October 29, 2012 by Amazon Customer
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