- Taschenbuch: 342 Seiten
- Verlag: John Wiley & Sons; Auflage: 1. Auflage (11. März 2005)
- Sprache: Englisch
- ISBN-10: 0470022981
- ISBN-13: 978-0470022986
- Größe und/oder Gewicht: 16,8 x 2 x 24,4 cm
- Durchschnittliche Kundenbewertung: 3 Kundenrezensionen
- Amazon Bestseller-Rang: Nr. 235.003 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)
- Komplettes Inhaltsverzeichnis ansehen
Statistics: An Introduction using R (Englisch) Taschenbuch – 11. März 2005
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"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)
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". It features step-by-step instructions that assume no mathematics, statistics or programming background, helping the non-statistician to fully understand the methodology. It 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.It covers the full range of statistical techniques likely to be needed 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. It 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. Alle Produktbeschreibungen
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Also, zusammengefasst, das "Statistics - Intoduction using R" von M.J.Crawley ist ein sehr gutes Buch, um sich die Grundlagen und den ein oder anderen "Trick" in der Statistik und in R anzueignen. Trotzdem ist es eine abgespeckte Version des "großen" Crawley (The R-Book) und für tiefgründigere und spezielle Fragestellungen nicht so geeignet. Aber dennoch sehr empfehlenswert!
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Using this book in combination with Google and some additional text books like Modern Statistics for the Life Sciences I've learnt a lot. Enough to design and analyze most of my experiments correctly. But it was not an easy ride... The book is incredibly chaotic. The author is using concepts that he will introduce only later... - sometimes 10 chapters later! I've wasted a lof of time trying to figure things out that would be abundantly explained in the following chapters. Obviously, if you are not as OCD as I am, it would not be an issue; but, honestly, once I've realized that, I was going way faster simply assuming that if something is not clarified now, it will be later (true for 80-90% of things). I highly recommend downloading supplementary material (granted, it is not under the link specified in the book but a little bit of search within the domain should yield results). It contains all the codes used in the book, and additional theory and exercises.
All in all, it was a bumpy ride but it did get me where I wanted. And I most definitely will use the book as a reference. 4 stars.
The book covers regression and ANOVA in-depth. The author has a strong knowledge of stats and applying R to solve and analyze stat problems.
As far as regression, it covers linear, non-linear, Single variable, multi-variable, regression verification (checking the model), F tests etc.
As others have mentioned, this book assumes the reader already has a good understanding of statistics, regression, ANOVA, and hypothesis testing etc.
1)The author has strong knowledge of stats and it shows.
2)Very helpful examples of R code (you can download the sample code online).
3)Succinct and to-the-point. There is no philosophical meandering which takes up time and space.
4)Good bang for the buck.
1)Not all the best R examples were provided. For example, the very highly useful descStat (from PERregress library) was never used as an example.
2)Assumes the reader already has good understanding of stats (not an unreasonable assumption).
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.