The Descriptive Statistics Report provides a summary of the statistics that are calculated for the cluster features. Figure 15-25 illustrates a sample Descriptive Statistics Report.
Workload Characterization Analysis Clustering Input Descriptive Statistics For: Thursday, June 19, 2003 Summary for Total Sample: Number of Sample Observations: 2,000 Feature Description Minimum Maximum Average Std. Dev. CV ________ _________________________________________ ____________ ____________ ____________ ____________ ______ JOBTCBTM Job TCB CPU Time 0:00:00.01 0:40:00.28 0:00:13.14 0:01:42.89 7.83 JOBEDASD Total EXCPs 1.00 1,154,523.00 5,992.30 34,582.08 5.77 Summary for Trimmed Sample: Number of Sample Observations: 1,919 Feature Description Minimum Maximum Average Std. Dev. CV ________ _________________________________________ ____________ ____________ ____________ ____________ ______ JOBTCBTM Job TCB CPU Time 0:00:00.01 0:01:00.81 0:00:03.74 0:00:07.71 2.06 JOBEDASD Total EXCPs 1.00 38,096.00 2,505.00 4,508.48 1.80
Figure 15-25. Descriptive Statistics Report
Note that the report has two sections: one for the original
sample randomly taken from the input data; and the other, the
sample data after trimming, using the Sample Trim Limit value
specified by the user on the Execution Options panel.
The Descriptive Statistics Report contains the following
fields:
OBSERVATIONS: The number of observations selected for
processing.
FEATURE: The feature name. The feature names that are
listed in the report correspond to the features
that are specified on the Workload
Characterization screen. In Neugents
technology, this is also called a "pattern".
DESCRIPTION: The SAS label of the selected data element,
from either the CA MICS GENLIB definition or
supplied by the user.
MINIMUM: The minimum value observed for the feature in
the sample.
MAXIMUM: The maximum value observed for the feature in
the sample.
AVERAGE: The average calculated for the feature
observations in the sample.
STD DEV: The standard deviation calculated for the
feature observations in the sample.
CV: The coefficient of variation. The CV is
calculated by dividing the standard deviation
by the average.
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