If two series vary in such a way, that fluctuations in one are accompanied by the fluctuations in the other, these variables are said to be correlated. Like rise in price of a commodity, reduces its demand and vica-versa. 

Some relationship exists between age of husband and wife, rainfall and production. Two variables are said to be correlated if the change in one variable results in a corresponding change in the other variable. 

According to A. M. Tuttle, “Analysis of co-variation of two or more variables is usually called correlation”. 

Types of Correlation : Correlation can be of following types - 

(i) Positive and Negative Correlation : If changes in two connected series is in the same direction, i.e. increase in one variable is associated with increase in other variable, the correlation is said to be positive. For example increase in father‘s age, increase in son‘s age. 

If the two related series change in opposite direction i.e. increase in one variable is associated with the corresponding decrease in other variable, the correlation is said to be negative. 

(ii) Linear an Non-Linear : If the amount of change in one variable tends to bear constant ratio of change in the other variable, the correlation is said to be linear. We get a straight line if the variables of these series are marked on graph paper.

  Correlation would be called non-linear or curvilinear if the amount of change in one variable does not bear a constant ratio to the amount of change in the other variable. For example if we double the amount of rainfall the production would not necessarily be doubled.

(iii) Simple, Partial and Multiple Correlation :  When only two variables are studied, it is called simple correlation. 

If the common effect of two or more independent variables on one dependent data series is studied, it is called multiple correlations. For example if the study of rain, soil, temperature on potato production per acre is studied then it is multiple correlation. 

On the other hand, in partial correlation we recognize more than two variables, but consider only two variables to be influencing each other, the effect of other influencing variables being kept constant.

Degree of Correlation : The interpretation of co-efficient of correlation is based on the degree of correlation. The coefficient may be in the following degrees - 

(i) Perfect Correlation : 

a) Perfect positive correlation (r) = +1 

 b) Perfect negative correlation (r) = -1 

(ii) Absence of Correlation or No Correlation : 

          r = o 

(iii) Limited Degree of Correlation : 

(a) High degree positive or negative                ±0.75 to 1.00 

(b) Moderate degree positive or negative        ±0.25 to 0.75 

(c) Low degree positive or negative                 0 to ±0.25