So far we used scatterplots to study the relationships between two
quantitative variables, we studied the overall pattern of the
relationship by looking at its direction (positive, negative, or
neither), form (linear, curvilinear etc), and strength (eg. stronger,
weaker).

We also noted that assessing the strength of a relationship just by
looking at the scatterplot is quite hard, and therefore we need to
supplement the scatterplot with some kind of numerical measure that
will help us assess the strength, this numerical measure we are going
to learn.

Lets have a look at following two images (from the course website) to
see why numerical measures are required along with scatterplot to
assess strength of linear relationship.

We can see that in both cases, the direction of the relationship is
positive and the form of the relationship is linear. What about the
strength? Recall that the strength of a relationship is the extent to
which the data follow its form.

At first glance it looks like strength of the first graph is
stronger but at course website clarifies, both graphs are for same
data, just drawn using two different scales!

The purpose of this example was to illustrate how assessing
the strength of the linear relationship from a scatterplot alone is
problematic, since our judgment might be affected by the range of
values that are plotted. This example, therefore, provides a
motivation for the need to supplement the scatterplot with a numerical
summary that will measure the strength of the linear relationship
between two quantitative variables.

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