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                         VISUALIZING DATA
                The New Frontier of Data Analysis
                       William S. Cleveland

                       Two-Day Short Course
        University of Maryland & University of Michigan

Due to the overwhelming popularity of Bill Cleveland's short course,
"Visualizing Data:  The New Frontier of Data Analysis", the JPSM will
sponsor the course again on October 16-17, 1995 at the Marriott
Residence Inn in Bethesda, Maryland.  The JPSM is The Joint University
of Maryland-University of Michigan Program in Survey Methodology.  The
course is based on Bill's book Visualizing Data (Hobart Press,
October, 1993).  More information about the course is provided below.

FEE & REGISTRATION
Registration is $285 for nonstudents and $135 for full-time students.
Participants will receive a copy of Bill's book and a notebook
containing copies of course transparencies.

To receive registration materials call 1-800-937-9320 or email
JPSM@UMICH.EDU

OBJECTIVES
Two decades of research in statistical graphics, and the revolution in
computer graphics, have combined to produce a new frontier of data
analysis.  The course will present visualization tools that provide
deep insight into the structure of data.  There are graphical tools
such as coplots, brushing, and banking to 45 degrees.  There are
fitting tools such as loess and bisquare.

But the material in the course is much more than a compendium of
useful tools.  It conveys an approach to data analysis that stresses
the use of visualization to thoroughly study the structure of data and
to check the validity of statistical models fitted to data.  The
result is a vast increase in what can be learned from data.

INSTRUCTOR
William S. Cleveland is a leading researcher in statistical methods.
His previous writings on visualization, many papers and two books,
have enjoyed a large audience.  Tools introduced in these writings are
widely used throughout the scientific community.

WHO SHOULD ATTEND
The course is intended for anyone who has data to analyze.  The
prerequisites are familiarity with basic statistics and the
least-squares method of fitting equations to data.  For example, an
introductory course in statistics that included the fundamentals of
regression analysis would be sufficient.

COURSE CONTENT
The presentation is organized into five sections according to the type of
data to be analyzed:
   Univariate data
   Bivariate data
   Trivariate data
   Hypervariate data
   Multiway data

All sections include applications of the visualization tools to data sets
from scientific studies.  This shows the role each tool plays in data
analysis, and the class of problems it solves.  It also demonstrates the
power of visualization; for many of the data sets, the tools reveal that
effects were missed in the original analyses or incorrect assumptions were
made about the behavior of the data.  And the applications convey the
excitement of discovery that visualization brings to data analysis.

Many tools are treated in the sections, including the following:

Coplots
Level Plots
Equal Count Algorithm
Loess Fitting
Banking to 45 degrees
Slicing
R-F Spread Plots
Robust Fitting
Bisquare
Box Plots
Color Encoding
Q-Q Plots
Shading
Surfaces
Wireframe Plots
S-L Plot
Quantile Plot
Cycle Plot
Cropping
Brushing
Anti-Aliasing
Grosse's Method
Cut-and-Stack Plot
Motion
Contour Plot
3D Plot
Dot Plot
Seasonal Loess Fitting
Stereo
Given Plot
M-D Plot
Perspective
Residual
Dependence Plots
Jittering
Scatterplot Matrix