What is a correlation matrix?
A correlation matrix is a table that indicates the correlation coefficients between variables. For example, let’s say there are two random variables, X and Y. In that case, the correlation matrix of X and Y will indicate:
Please note that the correlation coefficient of (X, X) or Corr(X, X) for a random variable X is always 1. So, the correlation matrix contains 1s diagonally.
What is a correlation coefficient?
The 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 absolute 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 correlation matrix using Python?
We can use the numpy module in Python to calculate the correlation matrix. We can also use the pandas Python library to calculate the correlation matrix. In this article, we will discuss both.
How to calculate the correlation matrix using the numpy Python module?
We can use the following Python code to calculate the correlation matrix between two random variables, X and Y, using the numpy Python module. …






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