Database Reference
In-Depth Information
With these interpretation parameters explained, there may be several things that Sarah
can do
in
order to take action based upon our model. A few options might include:
Dropping the Num_Occupants attribute. While the number of people living in a home
might logically seem like a variable that would influence energy usage, in our model it did
not correlate in any significant way with anything else. Sometimes there are attributes that
don't turn out to be very interesting.
Investigating the role of home insulation. The Insulation rating attribute was fairly strongly
correlated with a number of other attributes. There may be some opportunity there to
partner with a company (or start one…?) that specializes in adding insulation to existing
homes. If she is interested in contributing to conservation, working on a marketing
promotion to show the benefits of adding insulation to a home might be a good course of
action, however if she wishes to continue to sell as much heating oil as she can, she may
feel conflicted about participating in such a campaign.
Adding greater granularity in the data set. This data set has yielded some interesting
results, but frankly, it's pretty general. We have used average yearly temperatures and total
annual number of heating oil units in this model. But we also know that temperatures
fluctuate throughout the year in most areas of the world, and thus monthly, or even weekly
measures would not only be likely to show more detailed results of demand and usage over
time, but the correlations between attributes would probably be more interesting. From
our model, Sarah now knows how certain attributes interact with one another, but in the
day-to-day business of doing her job, she'll probably want to know about usage over time
periods shorter than one year.
Adding additional attributes to the data set. It turned out that the number of occupants in
the home didn't correlate much with other attributes, but that doesn't mean that other
attributes would be equally uninteresting. For example, what if Sarah had access to the
number of furnaces and/or boilers in each home? Home_size was slightly correlated with
Heating_Oil usage, so perhaps the number of instruments that consume heating oil in each
home would tell an interesting story, or at least add to her insight.
