9. MULTIVARIATE REGRESSION FORECASTING › 9.6 Case Studies › 9.6.2 IMS Control Region Case Study › 9.6.2.1 Control Parameters

9.6.2.1 Control Parameters

Figure 9-12 shows the control parameters used to generate the
final multilinear regression model of this case study.

/--------------------  Multivariate Regression Forecasting  --------------------\
|Command ===>                                                                   |
|Enter a ? in any data entry field for more information on valid values.        |
|Composing CA MICS Inquiry:  IMSOVH - IMS Control Region Overhead Study         |
|                                                                               |
|Report title ===> Capture ratios on test workload                              |
|                                                                               |
|Selection criteria:                                                            |
|             Dependent file  ===> IMS - IMS Analysis File                      |
|           Dependent element ===> CTLCPUTM                                     |
|                  Start date ===> _________ (ddmonyyyy)                        |
|                       SYSID ===> SYS2                                         |
|                        Zone ===> 1_______                                     |
|                   No. weeks ===> ____      (1-9999)                           |
|       Specify CAPAPU Values ===> N         (Y/N)                              |
|Save forecast                ===> ___       (YES/NO/AGE)                       |
|Independent file             ===> IMS - IMS Analysis File                      |
|Independent elements         ===> MPPCPUTM BMPCPUTM ________ ________ ________ |
|                                  ________ ________ ________ ________ ________ |
|                                                                               |
|Confidence limits (percent)  ===> __        (70/90/95)                         |
|Min R-square improvement     ===> 0.001     (0.000-0.100)                      |
|Delete observations          ===> N         (Y/N/R)                            |
|                                                                               |
\------------------------------------------------------------------------------/

 Figure 9-12.  IMS Control Region Case Study Control Screen

Note that the same resource file (IMS) was used for both the
dependent variable (CTLCPUTM) and the two independent
variables (MPPCPUTM and BMPCPUTM).  Note also that the
analyst previously generated and saved forecasts for each of
the independent variables, using one of the forecasting
routines of this component.  This is required for
Multivariate Regression Forecasting to produce forecasts of
the dependent variable.

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 study, the first execution of the program uses the
default value for Minimum R-squared improvement.  However,
this value is not fine enough to distinguish between the two
models listed in the Model Analysis Report, shown in Figure
9-13.  When this type of situation occurs, the algorithm
chooses the first model, because the second model failed to
provide the Minimum R-squared improvement you specified.  The
model that the stepwise regression process chooses is
therefore the one which uses only MPPCPUTM as the independent
variable.  In the example, the analyst decided to see the
results of a model that chose both MPPCPUTM and BMPCPUTM as
independent variables before committing to one or the other.
Therefore the program was rerun with a lower Minimum
R-squared improvement value to force the choice of the second
model.