Description
Book SynopsisOffers coverage of basic and advanced statistical methods, concentrating on graphical inspection, and featuring step-by-step instruction to help non-statisticians understand the methodology.
Trade Review"...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)
Table of ContentsStatistical 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
