Database Reference
In-Depth Information
If you were representing this as data, maybe for an important study on the
jumping ability of animals, your observation of Mr. Fox would look like this:
Jump date:
July 1, 2013, 10:13 a.m.
Jumper species:
Vulpes vulpes
Jumper color:
#826548
Jumper height at shoulder:
0.42 m
Jump height:
0.78 m
Number of jumps:
1
Target species:
Canis lupus familiaris
Target color:
#454244
Target height at shoulder:
0.71 m
For both the
jumper
and the
jumpee
, we've captured the
dimensions
of species
and color. These are characteristics of the fox and dog.
We've also described with numerical
metrics
other characteristics of the actors
as well as the action that was taken (one jump of 0.78 meters). We could cre-
ate calculated metrics. How high can the fox jump relative to his height? In
this case it's 0.78m / 0.42m. That's almost twice his height—bravo, Mr. Fox.
SuMMARIZED DATA
When you summarize data, you group data with the same dimensions together
and do something sensible with the metrics. Now imagine you've gone to a
zoo with the intent to observe animal bounciness. You've observed for days
and captured the leap of the tiger and the spring of the gazelle. You want to
summarize this data, so you group the animals by their dimensions and sum
up the metrics.
Some metrics are additive, like the total number of jumps. For others it makes
more sense to calculate averages or rates. It's more common to see data sum-
marized than raw.
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