The Model Analysis Report in Figure 9-13 shows two models for evaluation. The best one-variable model includes only MPPCPUTM as the independent variable, while there is only one two-variable model to evaluate.
CA MICS Capacity Planner ANALYSIS OF MULTIVARIATE REGRESSION MODELS MODEL OF: CTLCPUTM BASED ON: MPPCPUTM BMPCPUTM --------INDEPENDENT--ELEMENTS-------- R**2 F P INTERCEPT NAME COEFFICIENT F P -------- ------- ----- --------- -------- ----------- ------- ----- 0.98 5806.11 .0001 3:06:54.3 MPPCPUTM 0.293982 5806.11 .0001 0.98 3286.01 .0001 1:14:09.0 MPPCPUTM 0.277912 2763.89 .0001 BMPCPUTM 0.170892 17.50 .0001
Figure 9-13. IMS Control Region Case Study Model Analysis Report As we noted previously, on the first execution of the model the analyst used the default Minimum R-squared improvement value of 0.05, which resulted in a choice of the first model (the one-variable model). The re-execution of the analysis with a Minimum R-squared improvement value of 0.001 resulted in the two-variable model. Both models show excellent values of r-squared, F, and p, with the r-squared values for both models less than 1% apart. In a situation like this, it is hard to defend the choice of one model over the other on the basis of statistical evidence alone. The analyst decided to use the two-variable model since it takes into account both types of dependent regions and therefore (in the analyst's opinion) passes the common sense test with a marginally better score than does the one-variable model. The two-variable model indicates that the value of m1 is 0.277912 and the value of m2 is 0.170892. This means that each additional CPU second of MPP processor utilization will generate an additional 0.277912 CPU seconds of control region overhead. Similarly, each additional CPU second of BMP processor workload will generate an additional 0.17 CPU seconds of control region overhead.
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