Correlation analysis is a basic statistical approach that can capture relationships among pairs of variables. Such variables usually represent properties of objects whose values may be stored in columns of a database table. For example, in a database containing customers, we may want to measure the correlation between the variables "Age" and "Income" or between the variables "Income" and "TotalPurchase", and so on.
Correlations among variables can be negative or positive. There are different ways of computing correlations, but in most cases, the correlation is measured as a coefficient ranging from -1 to 1. A value close to 0 in this range indicates a lack of correlation. Values closer to the boundaries -1 or 1 indicate strong negative or positive correlations, respectively. Usually (though not exclusively) strong positive or negative correlations may indicate a causal relationship between the variables. For example, there may be a positive correlation between the number of hours of studying for an exam and the score obtained in that exam.
Computing the Correlation Coefficient
Given two variables x and y (e.g., "Age" and "Income"), the correlation
coefficient for x and y is given by the ratio of the covariance of
x and y to the product of standard deviations of x and y.
In other words:
The covariance cov(x, y) is the average of the products of deviations
from the mean in each of x and y:
In the above, each xi represents the value of x in the ith row, is the mean value of x, and n is the total number of values (rows).
As an example consider the following table with 3 attributes X, Y, and Z.
Performing the above correlation computation among all pairs of variables, would
result in the following correlation matrix:
Note that the diagonal is always 1 because each variable is perfectly correlated
with itself. In this case, there is also a perfect correlation between X
and Y (after rounding). However, Z shows a strong negative
correlation to both X and Y.
Copyright © 2013-2015, Bamshad Mobasher, DePaul University.