In our previous article, we discussed what covariance is and how to calculate the covariance matrix between random variables. We learned that if the covariance between two random variables, X and Y, is positive, then X and Y are positively related. In other words, if we increase the value of X, the value of Y will also increase.
And if the covariance between two random variables X and Y is negative, then X and Y are negatively correlated. In other words, if we increase the value of X, the value of Y will decrease.
If the covariance between two random variables, X and Y, is large, then that usually indicates a strong relationship between X and Y. But statistically, the value of covariance is a little difficult to interpret. So, instead of covariance, we use correlation to measure the relationship between two random variables.
What is the Pearson R correlation coefficient?
The Pearson R correlation coefficient between two random variables, X and Y, indicates the correlation between the two random variables. It is a value between –1 to +1. A positive correlation coefficient means X and Y are positively correlated. In other words, if we increase X, Y will also increase. And a negative correlation coefficient indicates X and Y are negatively correlated. In other words, if we increase X, Y will decrease.
If the value of the correlation coefficient is close to 0, that means X and Y are weakly correlated. And if the absolute value of the correlation coefficient is close to 1, that means X and Y are strongly correlated.
How to calculate the Pearson R correlation coefficient?
The Pearson R correlation coefficient between two random variables, X and Y, is calculated as follows:
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