Types of Correlation

Pearson Correlation Coefficient Formula
The most common formula is the Pearson Correlation coefficient used for linear dependency between the data sets. The value of the coefficient lies between -1 to +1. When the coefficient comes down to zero, then the data is considered as not related. While, if we get the value of +1, then the data are positively correlated, and -1 has a negative correlation.



Where n = Quantity of Information


Σx = Total of the First Variable Value


Σy = Total of the Second Variable Value


Σxy = Sum of the Product of first & Second Value


Σx2 = Sum of the Squares of the First Value


Σy2 = Sum of the Squares of the Second Value


Linear Correlation Coefficient Formula
The formula for the linear correlation coefficient is given by;





Sample Correlation Coefficient Formula
The formula is given by:


rxy = Sxy/SxSy


Where Sx and Sy are the sample standard deviations, and Sxy is the sample covariance.


Population Correlation Coefficient Formula
The population correlation coefficient uses σx and σy as the population standard deviations and σxy as the population covariance.


rxy = σxy/σxσy


Regression Analysis


Regression analysis is a set of statistical methods used for the estimation of relationships between a dependent variable and one or more independent variables. It can be utilized to assess the strength of the relationship between variables and for modeling the future relationship between them.


Regression analysis includes several variations, such as linear, multiple linear, and nonlinear. The most common models are simple linear and multiple linear. Nonlinear regression analysis is commonly used for more complicated data sets in which the dependent and independent variables show a nonlinear relationship.