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In learning and teaching statistics, how often do we wish for a book with the right balance between theory and applications? How often have we wondered whether it is possible to devise a textbook equally well suited to classroom use and individual study? Now we have a solution for those who want all of this in a book that treats the powerful S-PLUS software in an elegant and effective manner. In S-PLUS Programming Language and Applied Statistics, Samir Safi introduces the language, the most common techniques, and abundant applications. The profusion of examples and exercises make the book an…mehr

Produktbeschreibung
In learning and teaching statistics, how often do we
wish for a book with the right balance between
theory and applications? How often have we wondered
whether it is possible to devise a textbook equally
well suited to classroom use and individual study?
Now we have a solution for those who want all of
this in a book that treats the powerful S-PLUS
software in an elegant and effective manner.
In S-PLUS Programming Language and Applied
Statistics, Samir Safi introduces the language, the
most common techniques, and abundant applications.
The profusion of examples and exercises make the
book an excellent source as a course text book or as
a guide to self-study.
After introducing the basic concepts of S-PLUS, Safi
shows how to use the software for the statistical
techniques ordinarily encountered in conventional
applications. The explanations are supplemented by
ample illustrations of how to use S-PLUS in
particular settings. The last chapters on writing
functions and simulation are beyond what is covered
in many one-semester methods courses, but are very
useful for anyone who needs to use more
sophisticated techniques.
Dr. Mary Gray
Autorenporträt
Dr. Samir Safi got his PhD in Statistics, 2004 from the
American University in Washington, DC, USA. He has authored
several statistics research papers in the area of time series
analysis. He has worked on comparing estimators in regression
models with auto-correlated errors and studied when
Ordinary Least Squares (OLS) is efficient.