The most common coefficient of correlation is known as the Pearson product-moment correlation coefficient, or Pearson's
Pearson's correlation coefficient between two variables is defined as the covariance of the two variables divided by the product of their standard deviations. The form of the definition involves a "product moment", that is, the mean (the first moment about the origin) of the product of the mean-adjusted random variables; hence the modifier product-moment in the name.
Pearson's correlation coefficient when applied to a population is commonly represented by the Greek letter
Pearson's correlation coefficient when applied to a sample is commonly represented by the letter
An equivalent expression gives the correlation coefficient as the mean of the products of the standard scores. Based on a sample of paired data
Mathematical Properties
- The value of
$r$ is always between$-1$ and$+1$ :$-1\leq r \leq 1$ . - The size of the correlation
$r$ indicates the strength of the linear relationship between$x$ and$y$ . Values of$r$ close to$-1$ or$+1$ indicate a stronger linear relationship between$x$ and$y$ . - If
$r=0$ there is absolutely no linear relationship between$x$ and$y$ (no linear correlation). - A positive value of
$r$ means that when$x$ increases,$y$ tends to increase and when$x$ decreases,$y$ tends to decrease (positive correlation). - A negative value of
$r$ means that when$x$ increases,$y$ tends to decrease and when$x$ decreases,$y$ tends to increase (negative correlation). - If
$r=1$ , there is perfect positive correlation. If$r=-1$ , there is perfect negative correlation. In both these cases, all of the original data points lie on a straight line. Of course, in the real world, this will not generally happen. - The Pearson correlation coefficient is symmetric.
Another key mathematical property of the Pearson correlation coefficient is that it is invariant to separate changes in location and scale in the two variables. That is, we may transform
Example
Consider the following example data set of scores on a third exam and scores on a final exam:
Example
This table shows an example data set of scores on a third exam and scores on a final exam.
To find the correlation of this data we need the summary statistics; means, standard deviations, sample size, and the sum of the product of
To find (
Put the summary statistics into the correlation coefficient formula and solve for