Databases Reference
In-Depth Information
Scatter Plots
A scatter plot is useful for examining the relationship or correlations between X and
Y variables. Variables are said to be correlated if they have a dependency on or are
somehow influenced by each other. For example, for an IT company, the project team
size is often related to team proficiency index: the relationship that exists might be that as
project team size increases the team proficiency index decreases (a negative correlation).
A scatter plot is a good way to visualize these relationships in data.
Once you have plotted all of the data points using a scatter plot, you will be able to
visually determine whether data points are related. Scatter plots can help you gain a sense
of how spread out the data might be or how closely related the data points are, as well as
quickly identify patterns present in the distribution of the data.
Figure
9-14
shows a scatter plot taking into account two variables, “project team size”
and “team proficiency index.” It also shows the impact of these two variables with respect
to the “client satisfaction score.” You can see that the smaller the “project team size” the
higher the “team proficiency index” leading to higher “client satisfaction score.”
Figure 9-14.
Scatter plot graph depicting the relationships between various groups of
data points
Figure
9-15
shows a correlation matrix taking several variables into account. You can
draw many interesting inferences from the graph; for example, proficiency ratings of an
individual are associated with the number of SME reviews and white papers published,
less time on the bench (idle time). Similarly, the performance rating of an individual is
associated with the number of SME reviews, white papers published, and less time on
the bench. Another interesting observation: people who've published white papers and
who've contributed to assets creation and SME reviews get excellence awards.
