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This book offers a detailed application guide to XploRe - an
interactive statistical computing environment. As a guide it
contains case studies of real data analysis situations. It helps
the beginner in statistical data analysis to learn how XploRe works
in real life applications. Many examples from practice are
discussed and analysed in full length. Great emphasis is put on a
graphic based understanding of the data interrelations. The case
studies include: Survival modelling with Cox's proportional hazard
regression, Vitamin C data analysis with Quantile Regression, and
many others.
It is generally accepted that training in statistics must include
some exposure to the mechanics of computational statistics. This
learning guide is intended for beginners in computer-aided
statistical data analysis. The prerequisites for XploRe - the
statistical computing environment - are an introductory course in
statistics or mathematics. The reader of this book should be
familiar with basic elements of matrix algebra and the use of HTML
browsers. This guide is designed to help students to XploRe their
data, to learn (via data interaction) about statistical methods and
to disseminate their findings via the HTML outlet. The XploRe APSS
(Auto Pilot Support System) is a powerful tool for finding the
appropriate statistical technique (quantlet) for the data under
analysis. Homogeneous quantlets are combined in XploRe into
quantlibs. The XploRe language is intuitive and users with prior
experience of other sta tistical programs will find it easy to
reproduce the examples explained in this guide. The quantlets in
this guide are available on the CD-ROM as well as on the Internet.
The statistical operations that the student is guided into range
from basic one-dimensional data analysis to more complicated tasks
such as time series analysis, multivariate graphics construction,
microeconometrics, panel data analysis, etc. The guide starts with
a simple data analysis of pullover sales data, then in troduces
graphics. The graphics are interactive and cover a wide range of
dis plays of statistical data."
Classical time series methods are based on the assumption that a
particular stochastic process model generates the observed data.
The, most commonly used assumption is that the data is a
realization of a stationary Gaussian process. However, since the
Gaussian assumption is a fairly stringent one, this assumption is
frequently replaced by the weaker assumption that the process is
wide sense stationary and that only the mean and covariance
sequence is specified. This approach of specifying the
probabilistic behavior only up to "second order" has of course been
extremely popular from a theoretical point of view be cause it has
allowed one to treat a large variety of problems, such as
prediction, filtering and smoothing, using the geometry of Hilbert
spaces. While the literature abounds with a variety of optimal
estimation results based on either the Gaussian assumption or the
specification of second-order properties, time series workers have
not always believed in the literal truth of either the Gaussian or
second-order specifica tion. They have none-the-less stressed the
importance of such optimali ty results, probably for two main
reasons: First, the results come from a rich and very workable
theory. Second, the researchers often relied on a vague belief in a
kind of continuity principle according to which the results of time
series inference would change only a small amount if the actual
model deviated only a small amount from the assum ed model."
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