By: Michael J Crawley(Author)
761 pages, Figs, tabs
Offers extensive coverage of many basic and more advanced statistical methods, concentrating on graphical inspection, and features step-by-step instructions to help the non-statistician to understand fully the methodology.
...suitable as a reference book for experienced statisticians, a vehicle for learning the S statistical computing language, or a resource for statistics instructors... (The American Statistician, Vol. 58, No. 1, February 2004) "...especially useful as an introduction to a wide variety of data analysis techniques." (R News) "...The book is well written there is an air of common sense throughout and is at a level which ensures its usefulness for a wide range of readers..." (Zentralblatt Math, Vol. 1001, No.01, 2003) "...the book is a useful and practical introduction to many areas of statistical data analysis." (Computational STatistics & Data Analysis) "...surely not the last statistics book you'll ever need, but it might well be the first you will ever really use." (Basic Applied Ecology, Vol. 4, No. 3) "...recommended...contains a wealth of sage advice..." (Technometrics, Vol. 45, No. 4, November 2003) ...a practical introduction to statistics...does not cover all...sophisticated statistical and graphical features of the S Plus system, but provides a first class starting point and, probably, for most readers, a sufficient end point. (Quarterly of Applied Mathematics, LXI, No. 4, December 2003) a valiant and useful first attempt to present both statistics and S PLUS together (Journal of The Royal Statistical Society Vol.167 No.4)
Preface.Statistical methods. Introduction to S Plus. Experimental design Central tendency. Probability. Variance. The Normal Distribution. Power calculations. Understanding data: graphical analysis. Understanding data: tabular analysis. Classical tests. Bootstrap and jackknife. Statistical models in S Plus. Regression. Analysis of variance. Analysis of covariance. Model criticism. Contrasts. Split plot Anova. Nested designs and variance components analysis. Graphs, functions and transformations. Curve fitting and piecewise regression. Non linear regression. Multiple regression. Model simplification. Probability distributions. Generalised linear models. Proportion data: binomial errors. Count data: Poisson errors. Binary response variables. Tree models. Non parametric smoothing. Survival analysis. Time series analysis. Mixed effects models. Spatial statistics. Bibliography. Index.
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