Correlation Coefficient

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Correlation coefficients are used to measure how strong a relationship is between two variables. There are several types of correlation coefficient, but the most popular is Pearson's correlation. Pearson’s correlation (also called Pearson’s R) is a correlation coefficient commonly used in linear regression. If you are starting out in statistics, you’ll probably learn about Pearson’s R first. In fact, when anyone refers to the correlation coefficient, they are usually talking about Pearson’s.

Meaning

·         A correlation coefficient of 1 means that for every positive increase in one variable, there is a positive increase of a fixed proportion in the other. For example, shoe sizes go up in (almost) perfect correlation with foot length.

·         A correlation coefficient of -1 means that for every positive increase in one variable, there is a negative decrease of a fixed proportion in the other. For example, the amount of gas in a tank decreases in (almost) perfect correlation with speed.

·         Zero means that for every increase, there isn’t a positive or negative increase. The two just aren’t related.


Correlation coefficient formulas are used to find how strong a relationship is between data. The formulas return a value between -1 and 1, where:

·         1 indicates a strong positive relationship.

·         -1 indicates a strong negative relationship.

·         A result of zero indicates no relationship at all.

correlation coefficient formula









Video Link --> Correlation coefficient - Part -1

Video Link --> Correlation coefficient - Part -2


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