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Statistics : an introduction using R / Michael J. Crawley.

By: Crawley, Michael J.
Material type: TextTextPublisher: Chichester, West Sussex, England : J. Wiley, c2005Description: xiii, 327 p. : ill. ; 25 cm.ISBN: 0470022973 (acid-free : hardback); 9780470022979 (acid-free : hardback); 0470022981 (acid-free : pbk.); 9780470022986 (acid-free : pbk.).Subject(s): Mathematical statistics -- Textbooks | R (Computer program language)DDC classification: 519.5 Other classification: 31.73 | 70.03 | DAT 368f | MAT 620f | ST 601 R01 | WC 7000 | ST 601
Contents:
Fundamentals -- Dataframes -- Central tendency -- Variance -- Single samples -- Two samples -- Statistical modelling -- Regression -- Analysis of variance -- Analysis of covariance -- Multiple regression -- Contrasts -- Count data -- Proportion data -- Death and failure data -- Binary response variable -- Fundamentals of the R language.
Review: "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 Computational 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."--BOOK JACKET.
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Item type Current location Call number Status Date due Barcode
Book University of Texas At Tyler
Stacks - 3rd Floor
QA276.12 .C73 2005 (Browse shelf) Available 0000001792316

Includes bibliographical references (p. 305-308) and index.

Ch. 1. Fundamentals -- Ch. 2. Dataframes -- Ch. 3. Central tendency -- Ch. 4. Variance -- Ch. 5. Single samples -- Ch. 6. Two samples -- Ch. 7. Statistical modelling -- Ch. 8. Regression -- Ch. 9. Analysis of variance -- Ch. 10. Analysis of covariance -- Ch. 11. Multiple regression -- Ch. 12. Contrasts -- Ch. 13. Count data -- Ch. 14. Proportion data -- Ch. 15. Death and failure data -- Ch. 16. Binary response variable -- App. 1. Fundamentals of the R language.

"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 Computational 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."--BOOK JACKET.

Reviews provided by Syndetics

CHOICE Review

Intended for first-time undergraduate statistics students, this book offers a demanding, non-calculus-based coverage of such standard topics as hypothesis testing, modeling, regression, ANOVA, and count data. Crawley offers numerous graphical visualizations of data sets that are available online and includes ongoing development of pertinent R language coding and sample printouts. Unfortunately, this creates the delicate balance between presenting statistical concepts and insights simultaneously with the demands of R. As a result, the emphasis often lies on the numerical summaries and coding rather than the context from which the data originated and on which the statistical need arose. Subsequently, the reader might want to supplement this book with another containing a sole emphasis on the related statistical ideas to fully flesh out both aspects. In fairness, Crowley's many applied examples and data sets exhibit well-grounded legitimacy, especially when readers link the statistical results with what is being illustrated. However, the fast-paced presentation of terminology (such as discussions of hypothesis testing, p-values, and Type I and II errors in the first four pages of the book) will prove challenging to many. ^BSumming Up: Recommended. Upper-division undergraduates; graduate students; professionals. N. W. Schillow Lehigh Carbon Community College

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