This graph allows you to look for patterns (both linear and non-linear). When you investigate the relationship between two variables, always begin with a scatterplot.
Ignoring the scatterplot could result in a serious mistake when describing the relationship between two variables. Pearson’s linear correlation coefficient only measures the strength and direction of a linear relationship. Plot 2 shows a strong non-linear relationship. Plot 1 shows little linear relationship between x and y variables. Both of these data sets have an r = 0.01, but they are very different. Examples of negative correlation.Ĭorrelation is not causation!!! Just because two variables are correlated does not mean that one variable causes another variable to change.Įxamine these next two scatterplots. Examples of Negative Correlation Figure 7.