Capture Ratio - Control Parameters

Set control parameters as required to generate the your multivariate regression model.
rm
Using a short set of menus that are described in Online Facility Tutorial section, you will arrive at the Multivariate Regression Forecasting screen shown in Figure 9-9.
Figure 9-9 shows the control parameters that we used to generate the final multivariate regression model in this case study.
Figure 9-9. Capture Ratio Case Study Control Parameters
/-------------------- Multivariate Regression Forecasting --------------------\ |Command ===> | |Enter a ? in any data entry field for more information on valid values. | |Composing
MICS
Inquiry: CAPRAT - Capture ratios on test workload | | | |Report title ===> Capture Ratios on test workload | | | |Selection criteria: | | Dependent file ===> TOT - | | Dependent element ===> TOTCPUTM | | Start date ===> _________ (ddmonyyyy) | | SYSID ===> SYS1 | | Zone ===> 1_______ | | No. weeks ===> ____ (1-9999) | | Specify CAPAPU Values ===> N (Y/N) | |Save forecast ===> ___ (YES/NO/AGE) | |Independent file ===> WKL - WORKLOAD USAGE FILE | |Independent elements ===> BATCPUTM CICCPUTM IMSCPUTM TSOCPUTM ________ | | ________ ________ ________ ________ ________ | | | |Confidence limits (percent) ===> __ (70/90/95) | |Min R-square improvement ===> 0.001 (0.000-0.100) | |Delete observations ===> N (Y/N/R) | | | --------------------------------------------------------------------------------
Note that in this case study the analyst previously generated two resource element files. The first, TOT, contains a data element, TOTCPUTM, which represents the total systemwide CPU time. This file and this data element are specified on the control screen as the Dependent file and the Dependent element. The second file, WKL, contains the data elements that represent the CPU time consumed by each of the workloads running on the system. This file and the corresponding data elements are specified as the Independent file and Independent elements for this case study. Note also that the analyst previously generated and saved forecasts for each of the independent data elements, using one of the forecasting routines of this component. These forecasts are required for Multivariate Regression Forecasting to produce forecasts of the dependent variable.
The analyst also specified that the study would focus on SYSID SYS1 and Zone 1. Because no value is specified for the Confidence limits parameter, we will use the default value of 95%.
In this case study, a Minimum R-squared improvement value of .001 is used for the final model. Sometimes in the evaluation of multilinear models, you may feel that the default value of .05 does not quite provide a fine enough distinction between models to produce the best multilinear regression model. Normally, a value of .01 should be sufficient to make this distinction.
In this particular case study, the first evaluation of the model using the default Minimum R-squared improvement value resulted in values for m of Equation 8 in the Capture Ratio Case Study section that appeared to be somewhat high (for example, greater than 2). The analyst therefore attempted the same regression model, but with a Minimum R-squared improvement value of 0.001. This time the stepwise regression process produced a different model that provided m values which were closer to the true values. The regression process may provide different models with the same list of potential dependent variables, and the same input data, but with different Minimum R-squared improvement values. This is due to there being a model of marginally higher r-squared value that fits the data but which the regression process discarded as being irrelevant due to the default Minimum R-squared improvement value, but which it will not discard with a lower value.
There can be a significant amount of subjective judgment involved in the evaluation of regression models. It is highly unlikely that two analysts working independently with the same set of data would produce the same capture ratio coefficients. This does not mean that either of them is necessarily incorrect, since this method of capture ratio determination claims only to be an estimation technique. There is no generally accepted technique for determining what the exact capture ratios are on a specific processor. Since the uncaptured processor time is by definition not distributed to the various workloads by the operating system, there can be no method of independently checking the results. Thus, the results of any capture ratio analysis are only an estimate.