It is quite common to communicate the Correlation between two variables in Data Analysis. However, we should always represent the scatter plot apart from just the correlation. The reason for that is because correlation is quite sensitive to outliers and it cannot also capture parabolic patterns. Hence, although a high correlation indicates a strong linear relationship between those two variables, we need to be cautious that this measure can be misleading.
A great example for this case is the Anscombe’s quartet which comprises four datasets that have nearly identical simple descriptive statistics, yet have very different distributions and appear very different when graphed.
Below you can find the four datasets.
For all datasets:
|Mean of x||9||exact|
|Sample variance of x||11||exact|
|Mean of y||7.50||to 2 decimal places|
|Sample variance of y||4.125||±0.003|
|Correlation between x and y||0.816||to 3 decimal places|
|Linear regression line||y = 3.00 + 0.500x||to 2 and 3 decimal places, respectively|
|Coefficient of determination of the linear regression||0.67||to 2 decimal places|
But with totally different scatter plots!