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STEPWISE REGRESSION METHODOLOGY 265
x = ISO 14001 certification duration (months)
3
x = landfill disposal fee per ton in area (dollars)
4
x = recycling level (percentage of total waste generation)
5
x = days open per year
6
Nonlinear variables to be investigated were
2
x = number of employees at the facility squared
1
2
x = landfill disposal fee per ton in area (dollars) squared
4
2
x = recycling level (percentage of total waste generation) squared
5
Several indicator variables to be investigated were
x = indictor variable code 1 non–ISO 9000 certification and code 0 for certification
7
x = indictor variable code 1 non–ISO 14000 certification and code 0 for
8
certification
Indicator variables were used to investigate the effects of company location. The vari-
ables were investigated based on the U.S. state in which the company operated:
x through x = indictor variable for U.S. state of operation with code 1 for opera-
38
9
tion in state i and code 0 for operation in a different state
Several interaction variables to be investigated were
x x = interaction variable between number of employees and ISO 9000 duration
1 2
x x = interaction variable between number of employees and ISO 14000 duration
1 3
x x = interaction variable between number of employees and disposal cost
1 4
x x = interaction variable between number of employees and recycling level
1 5
x x = interaction variable between number of employees and days open per year
1 6
x x = interaction variable between number of employees and ISO 9000 indicator
1 7
x x = interaction variable between number of employees and ISO 14000 indicator
1 8
16.3.2 STEP 2: DETERMINE THE MULTIPLE REGRESSION
MATHEMATICS AND PROCEDURE
In fitting a multivariable regression model and when the number of variables exceeds
two, as in the case of these models, matrix theory can facilitate the mathematical
manipulations considerably (Walpole and Myers, 1993). Minitab was used as the soft-
ware program to conduct the regression analyses.