national multiple family submetering and allocation billing program ...

(in-rent), has an average commodity charge of $5 per kgal, and has 1 bedroom, the following
equation could be used:
19 .95 + 6.80 + 6.84 + 5*( −2.01)
+ 1*(17.44) = 40.98
From the Model #2 equation, the annual water use could be estimated for the property as
40.98 kgal per unit per year.
Model #3 –Nine Independent Variables Including Cost of Rent
The third multivariate regression model presented has slightly more predictive power
than the other models presented, but also represents a smaller sample of properties because it
includes a cost of rent variable. Inclusion of a cost of rent variable, by definition, excludes
properties that are non-rentals (condominiums, private resident owned, and other) as well as
those that did not respond to the question. Excluding these properties makes this model less
representative of the population of multi-family housing found in the US, which includes a mix
of ownership arrangements.
Model #3 includes nine independent variables identified as significant from the ANOVA
presented earlier in this chapter including:
! Average number of bedrooms per unit
! Year the property was built (1994 and earlier or 1995 and later)
! Average rent per bedroom
! Utility’s average commodity charge for water and wastewater
! Presence of a play area
! Presence of a cooling tower
! Classification as senior citizen/retirement community
! RUBS
! Submetering
These factors were selected because of their established significance in determining water
use and through an iterative stepwise regression process the statistical program evaluated the
impact of different variables and selected those that provided the best fit. Researchers then
modified the stepwise model to increase the sample size and include several other factors known
to be significant predictors of water use. In view of the sample size, model selection effects were
not deemed to be sufficiently important to be taken into account.
Fundamental information and statistics from the regression model are presented in Table
5.18. The adjusted coefficient of determination (R 2 ) for Model #3 is 0.260. This indicates that
the model explains about 26 percent of the variability in the data. The P-value for the model is
163