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Easily Use SAS to Produce Your Graphics Diagrams, plots, and other
types of graphics are indispensable components in nearly all phases
of statistical analysis, from the initial assessment of the data to
the selection of appropriate statistical models to the diagnosis of
the chosen models once they have been fitted to the data.
Harnessing the full graphics capabilities of SAS, A Handbook of
Statistical Graphics Using SAS ODS covers essential graphical
methods needed in every statistician's toolkit. It explains how to
implement the methods using SAS 9.4. The handbook shows how to use
SAS to create many types of statistical graphics for exploring data
and diagnosing fitted models. It uses SAS's newer ODS graphics
throughout as this system offers a number of advantages, including
ease of use, high quality of results, consistent appearance, and
convenient semiautomatic graphs from the statistical procedures.
Each chapter deals graphically with several sets of example data
from a wide variety of areas, such as epidemiology, medicine, and
psychology. These examples illustrate the use of graphic displays
to give an overview of data, to suggest possible hypotheses for
testing new data, and to interpret fitted statistical models. The
SAS programs and data sets are available online.
Easily Use SAS to Produce Your Graphics Diagrams, plots, and other
types of graphics are indispensable components in nearly all phases
of statistical analysis, from the initial assessment of the data to
the selection of appropriate statistical models to the diagnosis of
the chosen models once they have been fitted to the data.
Harnessing the full graphics capabilities of SAS, A Handbook of
Statistical Graphics Using SAS ODS covers essential graphical
methods needed in every statistician's toolkit. It explains how to
implement the methods using SAS 9.4. The handbook shows how to use
SAS to create many types of statistical graphics for exploring data
and diagnosing fitted models. It uses SAS's newer ODS graphics
throughout as this system offers a number of advantages, including
ease of use, high quality of results, consistent appearance, and
convenient semiautomatic graphs from the statistical procedures.
Each chapter deals graphically with several sets of example data
from a wide variety of areas, such as epidemiology, medicine, and
psychology. These examples illustrate the use of graphic displays
to give an overview of data, to suggest possible hypotheses for
testing new data, and to interpret fitted statistical models. The
SAS programs and data sets are available online.
Each chapter consists of basic statistical theory, simple examples
of S-PLUS code, plus more complex examples of S-PLUS code, and
exercises. All data sets are taken from genuine medical
investigations and will be available on a web site. The examples in
the book contain extensive graphical analysis to highlight one of
the prime features of S-PLUS. Written with few details of S-PLUS
and less technical descriptions, the book concentrates solely on
medical data sets, demonstrating the flexibility of S-PLUS and its
huge advantages, particularly for applied medical statisticians.
This book covers a range of statistical methods useful in the analysis of medical data, from the simple to the sophisticated, and shows how they may be applied using the latest versions of S-PLUS and S-PLUS 6. In each chapter several sets of medical data are explored and analysed using a mixture of graphical and model fitting approaches. At the end of each chapter the S-PLUS script files are listed, enabling readers to reproduce all the analyses and graphics in the chapter. These script files can be downloaded from a web site. The aim of the book is to show how to use S-PLUS as a powerful environment for undertaking a variety of statistical analyses from simple inference to complex model fitting, and for providing informative graphics. All such methods are of increasing importance in handling data from a variety of medical investigations including epidemiological studies and clinical trials. The mix of real data examples and background theory make this book useful for students and researchers alike. For the former, exercises are provided at the end of each chapter to increase their fluency in using the command line language of the S-PLUS software. Professor Brian Everitt is Head of the Department of Biostatistics and Computing at the Institute of Psychiatry in London and Sophia Rabe-Hesketh is a senior lecturer in the same department. Professor Everitt is the author of over 30 books on statistics including two previously co-authored with Dr. Rabe-Hesketh.
This book follows in the footsteps of The Mathematics Companion
and The Physics Companion, presenting an introduction to basic
probability and statistics through bite-size coverage of key
topics. It is designed as a revision aid and study guide for
undergraduate students, with descriptions of key concepts from
probability and statistics in self-contained sections. It also
makes an excellent reference for non-statisticians from various
scientific disciplines who need an easy-to-follow reference for
basic statistical techniques. The text shows how statistics can be
applied in the real world, with lots of interesting examples and
plenty of diagrams and graphs to illustrate the concepts more
clearly.
The majority of data sets collected by researchers in all
disciplines are multivariate, meaning that several measurements,
observations, or recordings are taken on each of the units in the
data set. These units might be human subjects, archaeological
artifacts, countries, or a vast variety of other things. In a few
cases, it may be sensible to isolate each variable and study it
separately, but in most instances all the variables need to be
examined simultaneously in order to fully grasp the structure and
key features of the data. For this purpose, one or another method
of multivariate analysis might be helpful, and it is with such
methods that this book is largely concerned. Multivariate analysis
includes methods both for describing and exploring such data and
for making formal inferences about them. The aim of all the
techniques is, in general sense, to display or extract the signal
in the data in the presence of noise and to find out what the data
show us in the midst of their apparent chaos. An Introduction to
Applied Multivariate Analysis with R explores the correct
application of these methods so as to extract as much information
as possible from the data at hand, particularly as some type of
graphical representation, via the R software. Throughout the book,
the authors give many examples of R code used to apply the
multivariate techniques to multivariate data.
The book is intended as a quick source of reference and as an
aide-memoir for students taking A-level, undergraduate or
postgraduate statistics courses. It includes numerous examples,
helping instructors on such courses by providing their students
with small data sets with which to work.
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