Statistics – Correlation:

Correlation is used to find relationships between quantitative variables or categorical variables.  A positive correlation indicates the extent to which those variables increase or decrease in parallel; a negative correlation (inverse correlation) indicates the extent to which one variable increases as the other decreases.

The degree of association is measured by a correlation coefficient, denoted by ‘r’ also called as Pearson’s correlation coefficient, which is a measure of linear association.

The correlation coefficient varies from + 1 through 0 to – 1. Complete absence of correlation is represented by 0.

When an investigator has collected two series of observations and wishes to see whether there is a relationship between them, first one should construct a scatter diagram. If one set of observations consists of experimental results and the other consists of observed (independent variable) classification, is measured along the horizontal axis and the experimental results (dependent variable) on the vertical axis.

For absolute values of r,

0-0.19 is regarded as very weak,

2-0.39 as weak,

0.40-0.59 as moderate,

0.6-0.79 as strong,

0.8-1 as very strong correlation.

Pearson correlation coefficient:

A correlation report also shows a second result of each test which is statistical significance.

In Excel we have the function CORREL(array 1, array 2)
For example, =CORREL(A2:A6,B2:B6)