national multiple family submetering and allocation billing program ...

Indoor Water Use per Unit in 2001 Indoor Water Use per Unit in 2002
Property Characteristic
Mean
(kgal)
Std.
Dev. N
P-
Value *
Mean
(kgal)
Std.
Dev. N
Government
subsidized
52.30 26.90 N=88 55.00 31.40 N=76
How is the Private rental 52.00 24.90 N=710 52.00 26.80 N=611
property
Condominium 50.30 24.90 N=170 0.940 51.00 27.20 N=167
classified? Privt. resident
owned
50.60 29.40 N=99 51.50 28.10 N=90
Other 52.10 24.90 N=43
48.50 21.10 N=42
How is the Non-Rental 50.7 26.3 N=312 50.8 26.6 N=299
property
.433
Rental 52.0 25.1 N=798
classified?
52.3 27.4 N=687
Sauna/steam room
no 51.50 25.50 N=1060 52.00 27.40 N=936
0.947
yes 51.70 25.50 N=103 50.40 25.90 N=97
Spa/hot tub
no 51.50 25.70 N=897
0.948
51.90 28.20 N=778
yes 51.60 24.70 N=266
51.60 24.20 N=255
* Derived from an ANOVA test, indicates the probability of seeing a result as or more extreme than that seen in the
sample. Factors with p-values less than 0.05 were considered “statistically significant,” meaning that if there were
no difference, the probability of seeing a result as or more extreme than that seen in the sample was less than 5%.
P-
Value *
0.755
.410
0.579
0.878
Continuous Variables
Continuous variables such as the average number of bedrooms per unit and average rent
per bedroom could not be examined using ANOVA techniques so Pearson Correlation analysis
was used. The results from this analysis examining the impacts of the continuous variables from
the manager survey on per unit water use are shown in Table 5.13.
The Pearson product moment coefficient of correlation is a measure of the strength of the
linear relationship between two variables - in this case property characteristics measured on a
continuous scale from the manager survey and average water use per unit per year (McClave, et.
al., 1997). In Table 5.13, the Pearson Correlation value is a number between –1 and 1 where a
value close to zero indicates that there is no correlation. The hypothesis is that the Pearson
Correlation is equal to zero, hence a p-value of 0.05 or less indicates that there is a less than 5%
chance that a correlation as or more extreme than the one observed could have occurred if there
were no relationship between the variables. All of the calculations assume that normal
approximations hold so that the lack of a linear relationship is equivalent to independence.
Again the dark line in Table 5.13 indicates the break point for statistically significant factors
selected for inclusion in multiple linear regression models.
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