Download Spotfire S+ User`s Guide for UNIX

Visualizing Two-Dimensional Data
VISUALIZING TWO-DIMENSIONAL DATA
Two-dimensional data are often called bivariate data, and the
individual, one-dimensional components of the data are referred to as
variables. Two-dimensional plots help you quickly grasp the nature of
the relationship between the two variables that constitute bivariate
data. For example, you might want to know whether the relationship
is linear or nonlinear, if the variables are highly correlated, if there
any outliers or distinct clusters, etc. In this section, we examine a
number of basic plot types useful for exploring a two-dimensional
data object.
•
Box Plot: a graphical representation showing the center and
spread of a distribution, as well as any outlying data points.
•
Strip Plot: a one-dimensional scatter plot.
•
QQ Plot: a powerful tool for comparing the distributions of
two sets of data.
When you couple two-dimensional plots of bivariate data with onedimensional visualizations of each variable’s distribution, you gain a
thorough understanding of your data.
Box Plots
A box plot, or box and whisker plot, is a clever graphical
representation showing the center and spread of a distribution. A box
is drawn that represents the bulk of the data, and a line or a symbol is
placed in the box at the median value. The width of the box is equal
to the interquartile range, or IQR, which is the difference between the
third and first quartiles of the data. The IQR indicates the spread of
the distribution for the data. Whiskers extend from the edges of the
box to either the extreme values of the data, or to a distance of
1.5 × IQR from the median, whichever is less. Data points that fall
outside of the whiskers may be outliers, and are therefore indicated
by additional lines or symbols.
By default, Spotfire S+ generates horizontal box plots from the menu
options. If you require vertical box plots, you should use the
command line function boxplot.
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