Overview of Data Analysis in SAS Visual Analytics Explorer
Types of Data Analysis
SAS Visual Analytics
enables you to perform three basic types of data analysis:
Correlation
identifies the degree
of statistical relationship between measures.
Fit Line
plots a model of the
relationship between measures. There are many types of fit lines,
including linear fit, quadratic fit, cubic fit, and penalized B-spline.
Forecasting
estimates future values
for your data based on statistical trends.
Correlation
Correlation identifies
the degree of statistical relationship between measures. The strength
of a correlation is described as a number between -1 and 1. A value
that is close to -1 implies a strong negative correlation, a value
that is close to 0 implies little or no correlation, and a value that
is close to 1 implies a strong positive correlation.
To apply correlation
to a visualization, add a linear fit line, or select the correlation
matrix visualization type.
For a heat map or a
simple scatter plot, the correlation is identified by a text label
in the visualization legend. Select 
to view additional details about the correlation,
including the exact correlation value.
to view additional details about the correlation,
including the exact correlation value.
For a scatter plot matrix,
the correlation for each plot is identified by a colored border around
the plot. The visualization legend displays a key for the color values.
Select to view additional details about the correlation,
including the exact correlation values for each plot.
to view additional details about the correlation,
including the exact correlation values for each plot.
Note: For nonlinear fit types,
a scatter plot matrix displays additional plots to show each intersection
of variables in two orientations. For example, if a scatter plot matrix
plots the variables A, B, and C, then plots are created for both A
* B and B * A when a nonlinear fit line is applied.
For a correlation matrix,
the correlation for each cell is identified by the color of the cell
background. The visualization legend displays a key for the color
values. The data tip for each cell displays the correlation value.
Fit Lines
A fit line plots a model
of the relationship between measures. You can apply fit lines to scatter
plots and heat maps.
You can apply the following
types of fit line to your visualization:
Best Fit
selects the most appropriate
model (linear, quadratic, or cubic) for your data. The Best
Fit method uses backward variable selection to select
the highest-order model that is significant. To see the final model
that was used, select from the visualization legend.
from the visualization legend.
Linear
creates a linear fit
line from a linear regression algorithm. A linear fit line produces
the straight line that best represents the relationship between two
measures. For more information about the linear fit line, select from the visualization legend.
from the visualization legend.
For a linear fit, correlation
is automatically added to the visualization. Correlation is not available
with other fit types.
Quadratic
creates a quadratic
fit line. A quadratic fit produces a line with a single curve. A quadratic
fit line often produces a line with the shape of a parabola. For more
information about the quadratic fit line, select from the visualization legend.
from the visualization legend.
Cubic
creates a cubic fit
line. A cubic fit line produces a line with two curves. A cubic fit
line often produces a line with an “S” shape. For more
information about the cubic fit line, select from the visualization legend.
from the visualization legend.
PSpline
creates a penalized
B-spline. A penalized B-spline is a smoothing spline that fits the
data closely. A penalized B-spline can display a complex line with
many changes in its curvature. For more information about the penalized
B-spline, select from the visualization legend.
from the visualization legend.
Forecasting
Forecasting estimates
future values for your data based on statistical trends. Forecasting
is available only for line charts that contain date or datetime data
items.
A forecast adds a line
with predicted values to your visualization and a colored band that
represents the confidence interval. For example, a 95% confidence
interval is the data range where the forecasting model is 95% confident
what the future values will be.
The explorer automatically
tests multiple forecasting models against your data, and then selects
the best model. To see which forecasting model was used, select from the visualization legend.
from the visualization legend.
The forecast model can
be any one of the following:
-
Damped-trend exponential smoothing
-
Linear exponential smoothing
-
-
Simple exponential smoothing
-
Winters method (additive)
-
Winters method (multiplicative)
Note: Forecasting accounts for
cyclical patterns by using standard intervals of time (for example,
60 minutes in an hour, 24 hours in a day, and so on). If your data
uses nonstandard intervals (for example, 48 30-minute cycles per day),
then cyclical patterns are not considered in the forecast.
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