HP Matrix Operating Environment Software - Manual (Page 33)

Linear regression
The linear regression is based on a least squares fit that minimizes the sum of the squares of the
vertical offsets between each of the aggregate points and the trend line that describes them.
TIP: Regressions performed over small data sets are not always meaningful and can be
misleading. Any trend analysis based on less than a dozen aggregate points should be carefully
compared with the historical data to see if it "makes sense." The maximum number of data points
for the trend analysis is the total time for the report divided by the business interval, because
business intervals can be excluded if they do not meet the validity criteria.
Because the trend is reported as an annual growth rate, it is best to have more than a year of
historical data before trying to analyze trends.
Error analysis
You can choose to include error analysis in the report. The following error value is available:
r-squared: r
2
is the square of the correlation coefficient (r), and is used in the 'goodness of fit'
analysis of trend estimations. r is a value between 0 and +/- 1. where values approaching +/- 1
indicate increasing validity of the data representation.
Forecasting growth
HP Insight Capacity Advisor software forecasting allows you to combine a range of historical
data (the
forecast data range ) with a predicted trend (the annual projected growth rate ) to produce
a forecast model . The forecast model can be used to provide an estimate of future utilization.
Whenever a Capacity Advisor report or profile is generated with an end date later than the
current date, the historical utilization data must be projected into the future. The projection is
indicated in the utilization graphs by a colored background. This projection is done based on a
forecast model . Forecast models can be defined globally, for individual workloads or systems, for
a scenario, and for individual workloads within a scenario. Because the process for defining a
forecast model is basically the same regardless of where it is in the hierarchy of forecast models,
the procedures below are broken into two parts: accessing the forecast model and defining it.
The forecast model hierarchy
The forecast model can be specified at four different levels within Capacity Advisor, with more
specific forecast models overriding more general models, as indicated in the following table:
Table 3-3 Forecast models
Overrides Description Forecast
• Nothing Applies to all workloads in Capacity
Advisor for which a more specific
forecast is not provided. Global Forecast
• Global Applies to a specific workload in
Capacity Advisor unless a more
specific forecast is provided. Workload Forecast
• Global
• Workload Applies to all workloads within a
Capacity Advisor scenario for which
a more specific forecast is not
provided. Scenario Forecast
• Global
• Workload
• Scenario Applies to a specific workload within
a Capacity Advisor scenario. Scenario Workload Forecast
Forecasting growth 33