Scatterplots, like histograms, are a good visual means to understanding patterns of bivariate numerical data. Construction of a scatterplot is straightforward: each point on a scatterplot corresponds to one bivariate observation.
The scatterplot gives us a visual means of seeing relationships between the two variables. We call a relationship positive if an increase in one variable corresponds to an increase in the other. When one variable increases and the other decreases, we call the relationship negative.
What can a scatterplot tell us? In general terms, it gives us an idea of what kind of relationships (or patterns) our bivariate data has. We may have
Scatterplots can also give us visual evidence of outliers or suspicious observations (details in Weeks 12 and 13).
NOTE: Scatterplots are used only for quantitative variables (those that are comparable numerically). Examples of quantitative variables are: height, weight, rates, counts, etc. Examples of qualitative variables (those which can not be compared numerically) are: color, type of car, sex, etc.
Just like other graphical methods we've discussed, e.g., histograms, there are numerical statistics which give us a more precise description of bivariate relationships. The two major ones we'll discuss are correlation and linear regression.