For example, you may have noticed that on days that you sleep well, you are more active. Statistics can help quantify this relationship. A common statistic used to measure the strength of a relationship is called the "correlation coefficient", which ranges from -1 to 1. A correlation coefficient close to 1 indicates a strong positive relationship (as one variable increases, the other also increases), while a coefficient close to -1 indicates a strong negative relationship (the variables move in opposite directions).

Here, you can calculate correlations between different columns in your dataset, either individually or calculate all of them in a graphical correlation matrix. The correlation matrix provides a comprehensive overview of how all numeric variables in your dataset relate to each other. The diagonals of the matrix represent perfect correlations of variables with themselves (value of 1), while the off-diagonal values indicate the strength and direction of relationships between different variable pairs.