Michael J. Crawley, FRS, is with the Department of Biological Sciences, Imperial College of Science, Technology and Medicine. He is the author of three bestselling Wiley statistics titles and five life science books.

"...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)

• 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

Michael J. Crawley, FRS, is with the Department of Biological Sciences, Imperial College of Science, Technology and Medicine. He is the author of three bestselling Wiley statistics titles and five life science books.

"...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)

• 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