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national multiple family submetering and allocation billing program ...

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Factors that Influence Water Use<br />

The process of determining the impacts of water <strong>billing</strong> methods on water use began with<br />

an examination of the factors that may influence water use other than <strong>billing</strong> method. Using<br />

responses to the manager survey, linked with the cleaned historic <strong>billing</strong> data provided by each<br />

participating water provider, it was possible to evaluate the significance of a wide variety of<br />

factors on per unit per year water use. Wherever possible these factors were evaluated using<br />

historic <strong>billing</strong> data from 2001 <strong>and</strong> 2002. Only factors that were significant at the 95%<br />

confidence level in both years were considered for use in analytic models to evaluate the impacts<br />

of water <strong>billing</strong> methods.<br />

Categorical Variables<br />

Analysis of Variance (ANOVA) tests were performed to determine which of the nominal<br />

variables from the manager survey is statistically significant in explaining water use. Results<br />

from these analyses are presented in Table 5.12. The mean annual per unit water use (in kgal)<br />

for each set of property factors is shown along with the st<strong>and</strong>ard deviation (kgal) <strong>and</strong> the sample<br />

size. The p-value comes from the ANOVA test. Factors with p-values less than 0.05 were<br />

considered “statistically significant,” meaning that if there were no difference, the probability of<br />

seeing a result as or more extreme than that seen in the sample was less than 5%. Only factors<br />

with a p-value of 0.05 or less in both 2001 <strong>and</strong> 2002 were selected for use in the <strong>multiple</strong> linear<br />

regression models. The factors in Table 5.12 are sorted by order of statistical significance with<br />

the most significant factors listed first. The dark line indicates the break point for factors<br />

selected for inclusion in advanced regression models.<br />

Statistically significant categorical variables that were associated with per unit indoor<br />

water use in this sample of multi-<strong>family</strong> properties included:<br />

! Year property was built (1994 or earlier, 1995 or later)<br />

! Whether the property was a senior citizen/retirement community<br />

! Presence of a swimming pool<br />

! Low-flow (LF) faucet aerators<br />

! ULF toilets<br />

! Washing machine replacement<br />

! Presence of a play area<br />

! Presence of basketball court<br />

! Presence of cooling tower<br />

! Presence of food service facility or restaurant<br />

144

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