Figure 9-1 presents a sample Model Analysis Report for Multivariate Regression Forecasting. The Model Analysis Report provides a summary of the regression analysis that was conducted for the specified multivariate regression model.
CA MICS Capacity Planner ANALYSIS OF MULTIVARIATE REGRESSION MODELS MODEL OF: TOTCPUTM BASED ON: TSOCPUTM IMSCPUTM CICCPUTM --------INDEPENDENT--ELEMENTS-------- R**2 F P INTERCEPT NAME COEFFICIENT F P -------- ------- ----- --------- -------- ----------- ------- ----- 0.97 1127.97 .0001 10:57:58.3 TSOCPUTM 1.77499 71.77 .0001 IMSCPUTM 1.14392 77.90 .0001 CICCPUTM 1.13065 31.88 .0001
Figure 9-1. Model Analysis Report
The Model Analysis Report contains the following information:
MODEL OF: The dependent data element that you wish to
forecast.
BASED ON: The independent data elements on which the
analysis is based.
The first four columns of the report are discussed below.
R**2: The r-squared value for the model. Although
the SAS REG procedure attempts to use every
one of the independent elements suggested by
the analyst, the procedure excludes any term
that does not result in a minimum improvement
in the r-squared value as specified on the
Multivariate Regression Forecasting screen. We
recommend an r-squared value of 0.70 and above
for the acceptance of models produced with
Multivariate Modeling Forecasting.
F: The F statistic for the model. As discussed
in Section 9.2.3, the F statistic is calculated
from the ratio of sums of squares of the model.
Larger F values indicate a more reliable model.
The F statistics may be unreliable for models
developed with a small number of points (that
is, less than 30). An F value is provided for
each step made by the regression procedure.
P: The probability that the model proposed is
significant (that is, more reliable than
forecasting the average value of the historical
observations). Although most statistics texts
recommend testing the null hypothesis to a
0.001 level, we recommend 0.01 for models based
on computer measurement data. A p value is
provided for each step made by the regression
procedure.
INTERCEPT: The b value for the regression equation.
The next four columns are produced for each independent
element that you specify. These columns contain the
following information:
NAME: The name of the independent element being
analyzed.
COEFF: The coefficient for the independent element in
the regression equation. The coefficient is
the M value mentioned in Sections 9.2.1 and
9.4. Negative coefficients indicate that you
can expect resource consumption to decrease
with increases in the independent element
value. This situation is usually not normal in
resource consumption models.
F: The F statistics calculated for the business
element. This F value indicates the
significance of the independent element in the
model that was developed. Once again, larger
values indicate a more reliable model and the
value may be misleading for models based on
small samples.
P: The probability that the specified business
element makes a significant contribution to the
model developed. We recommend a 0.01
acceptance value.
This report is used to evaluate whether the model that is
produced adequately models an application. Although the
values of r-squared, F, and p indicate the statistical
significance of the proposed model, remember that a proper
evaluation includes all three tests enumerated in Section
9.2.3: statistical interpretation, visual inspection, and
the common sense test.
|
Copyright © 2014 CA.
All rights reserved.
|
|