national multiple family submetering and allocation billing program ...

substantially reduces the sample size since about 25% of respondents to the manager survey
didn’t answer that particular question, especially among individually-owned properties, where
managers may not know for what amount the units would rent.
Fundamental information and statistics from the regression model are presented in Table
5.16. The adjusted coefficient of determination (R 2 ) for Model #2 is 0.245. This indicates that
the model explains about 25 percent of the variability in the data. The P-value for the model is
statistically significant at the 95% confidence level.
Table 5.16 Model #2 summary statistics, coefficient of determination, and significance
R Adjusted R Std. Error of Degrees of
R
F P-value
Squared Square the Estimate Freedom
0.502 0.252 0.245 21.397 952 35.366 0.000
Predictors: (Constant), submetering, utility’s commodity average charge for water and wastewater, rental property
(compared non-rental property), cooling tower?, play area?, is property considered a senior citizen/retirement
community?, RUBS, property built before 1995 (compared to properties built 1995 or later), average number of
bedrooms per unit
Dependent Variable: Indoor water use per unit (average 2001, 2002)
The coefficients presented in Table 5.17 present the magnitude of the “effect” of the
different independent variables in the model. Of particular interest are the coefficients for RUBS
and submetering. In Model #2, eight of the nine independent variables were statistically
significant. The only factor that wasn’t statistically significant was RUBS. The B coefficient
shows the magnitude of the effect, and is graphically displayed in Figure 5.12 and Figure 5.13.
For submetering the B coefficient was –8.0 indicating that submetered properties used 8.0 kgal
per unit less water than in-rent properties after adjusting the other significant independent
variables. This effect was statistically significant at the 95% confidence level.
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