3. Reports and Graphics › 3.3 Statistical Analyses › 3.3.7 Stepwise Regression Report
3.3.7 Stepwise Regression Report
The Stepwise Regression Report provides insight into the
relationships between one measurement item and one or more
other measurement items. The report produces a model, or
formula, for deriving values of a measurement item from
values of one or more other measurement items (for example,
Z=aX+bY+c). The option that produces the report provides
five methods for stepwise regression analysis.
Parameters are provided for regression technique,
computational parameters, and report format. The parameters
allow you to do the following:
o Choose forward or backward regression
o Specify the minimum and maximum R-square values for
inclusion (or exclusion)
o Define entry significance levels and stay levels for use
in model building
o Force selected entries to be used in the model
o Specify the minimum and maximum number of variables to be
used in the model
o Show model building statistics
An analysis of variance report is produced for each step in
the procedure.
Figures 3-25 and 3-26 show the first, third, and last pages
of a sample stepwise regression report. The regression
produced a model for CICS CPU time (CSYCPUTM) based on the
number of I/O access method calls (CSYSIOCT), the number of
CICS output messages (CSYOMSGS), and the number of CICS
transactions processed (CSYTRANS).
To create a stepwise regression report, select the Stepwise
Regression Report option from either of the following menus:
o Direct Inquiry Statistical Analysis Menu
o Structured Inquiry Statistical Analysis Menu
(First page)
STEPWISE REGRESSION PROCEDURE FOR DEPENDENT VARIABLE CSYCPUTM
STEP 1 VARIABLE CSYSIOCT ENTERED R SQUARE = 0.34202814
C(P) = 22.10532991
DF SUM OF SQUARES MEAN SQUARE F PROB>F
REGRESSION 1 3808.14253047 3808.14253047 24.95 0.0001
ERROR 48 7325.86111002 152.62210646
TOTAL 49 11134.00364049
B VALUE STD ERROR TYPE II SS F PROB>F
INTERCEPT 4.32510779
CSYSIOCT 0.00778661 0.00155884 3808.14253047 24.95 0.0001
(Third page)
STEPWISE REGRESSION PROCEDURE FOR DEPENDENT VARIABLE CSYCPUTM
STEP 3 VARIABLE CSYTRANS ENTERED R SQUARE = 0.56032271
C(P) = 3.51010231
DF SUM OF SQUARES MEAN SQUARE F PROB>F
REGRESSION 3 6238.63511260 2079.54503753 19.54 0.0001
ERROR 46 4895.36852789 106.42105495
TOTAL 49 11134.00364049
B VALUE STD ERROR TYPE II SS F PROB>F
INTERCEPT 7.88105857
CSYTRANS -0.27525872 0.06954592 1667.11809796 15.67 0.0003
CSYSIOCT 0.01014079 0.00156759 4453.53773981 41.85 0.0001
CSYOMSGS 0.32783844 0.07301867 2145.25753545 20.16 0.0001
Figure 3-25. Sample Stepwise Regression Report (Part 1)
(Last page)
STEPWISE REGRESSION PROCEDURE FOR DEPENDENT VARIABLE CSYCPUTM
NO OTHER VARIABLES MET THE 0.1500 SIGNIFICANCE LEVEL FOR ENTRY INTO THE MODEL.
SUMMARY OF STEPWISE REGRESSION PROCEDURE FOR DEPENDENT VARIABLE CSYCPUTM
VARIABLE NUMBER PARTIAL MODEL
STEP ENTERED REMOVED IN R**2 R**2 C(P)
1 CSYSIOCT 1 0.3420 0.3420 22.1053
2 CSYOMSGS 2 0.0686 0.4106 17.0086
3 CSYTRANS 3 0.1497 0.5603 3.5101
VARIABLE
STEP ENTERED REMOVED F PROB>F LABEL
1 CSYSIOCT 24.9514 0.0001 ACCESS METHOD CALLS
2 CSYOMSGS 5.4672 0.0237 OUTPUT MESSAGES
3 CSYTRANS 15.6653 0.0003 TRANSACTIONS PROCESSED
Figure 3-26. Sample Stepwise Regression Report (Part 2)