1.19 : Correlation and Regression

In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a negative correlation, where the two variables move in opposite directions. A value closer to +1 or −1 suggests that the two variables are strongly correlated, directly or inversely. A value close to zero implies no linear correlation between the two variables.

To obtain the correlation coefficient and the best-fit equation, statisticians use the method of linear regression. The best-fit equation can be used subsequently to predict the value of a signal ('y' in the linear equation) or calculate the concentration of the substance giving the signal ('x' in the linear equation).