national multiple family submetering and allocation billing program ...

group of data, or a line that represents the best fit between two variables. Thus, by definition,
there will always be data points above and below values predicted by even the best models. To
appreciate this, just glance ahead to Figure 5.4 and Figure 5.5. These show how best fit lines are
created for large sets of data that show a relationship between water use and the number of
dwelling units present. The lines, if shown by themselves on these figures would give the
appearance of great precision, however, when one looks at the scatter in the data it is clear that
the model will not predict water use for any specific site very well, but will predict water use for
a large group much better.
So, when the analysis shows that there is a 95% confidence level that there will be a
specified difference in the average water use between two groups this should be thought of not as
a prediction that water use of individual members of the group will vary by this amount, since
due to the distribution of the data they might not, but as a prediction that there will be a 95%
probability that the average water use of a number of examples chosen from the two groups will
vary by this amount. From the perspective of any planning or policy study that deals with large
groups the ability to understand group dynamics is the key to good decision making.
Summary of Findings on Water Savings
To reach a conclusion regarding how water use differs between billing types, seven main
analyses were conducted. The number of properties included in each analysis is included in
Table 5.1. The results of each analysis are discussed in the sections that follow. As the reader
reviews the findings of each analysis, it may be helpful to refer back to Table 5.1, Table 5.2, and
Table 5.3, as they summarize the relevant findings and can help to avoid confusing the various
analyses.
Table 5.1 Number of properties included in each analysis, by billing type
Description of
Number of Properties by Billing Method (n)
Analysis In-Rent Sub. RUBS HWH Total
Postcard Survey 6493 273 595 41 7402
Manager Survey 858 118 177 22 1175
Statistical Model #1 705 101 150 - 956
Statistical Model #2 703 100 150 - 953
Statistical Model #3 531 79 136 - 746
Matched Pair 29 21* 14 - 64
Pre-Post Conversion - 6 39 1 46
* 7 HWHs were grouped with the submetered for this analysis
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