**The most widely used Correlation Co efficient is Pearson r. it is also called as linear or product moment correlation.**

**Simple Linear Correlation: **the Pearson Correlation assumes that both the variables get to be measured at the least interval scales and it also determines the extent to which both the values are proportionate to each other. The value if the correlation has no dependency on the specific measurement units that are used. To explain better, the correlation between the height and weight would continue to remain the same whether the measuring unit is inches and pounds or centimetres and kilograms. *Proportional *means *linearly related*, the correlation is high and it can be summarized by a straight line.

The line depicted in the above diagram is called the regression line or also sometimes the least squares line. The name is so because it helps to determine that the sum of the square distances of all data points from the line is the lowest possible. It is important to learn how to interpret the values of the correlation. It is already known that the correlation coefficient determines the relationship that is there between the two variables.

Ways to interpret the values of correlation: It has been explained earlier that the correlation coefficient is a representation of the linear relationship that exists between any two variables. When we derive the square of the correlation coefficient, we get the resulting value which is the r^{2. }It represents the proportion of the common variation that exists between two variables. This helps to know the strength and the significance that is there in the correlation. The significance value is the main resource which helps to know about the reliability that exists in the correlation and the magnitude of the correlation coefficient would change depending upon the size of the sample from which it is calculated.