How is correlation best defined?

Correlation is best defined as a measure of how well two factors predict together. In statistical terms, correlation indicates the strength and direction of a linear relationship between two variables. When two variables are correlated, changes in one variable are associated with changes in the other variable, which can be either positive or negative.

In a positive correlation, as one variable increases, the other variable also tends to increase, while in a negative correlation, an increase in one variable tends to be associated with a decrease in the other variable. The degree of this relationship is quantified by the correlation coefficient, which takes a value ranging from -1 to 1. A correlation coefficient close to 1 implies a strong positive correlation, while a coefficient close to -1 indicates a strong negative correlation.

This definition allows data analysts and scientists to understand the relationship between variables, make predictions, and draw conclusions based on their data analysis, which is a key aspect of data analytics and interpretation.

Other options do not capture the essence of correlation as succinctly. They focus on different concepts such as independent factors, group differences, or techniques for data visualization, which are not directly related to the statistical measure of the relationship between two variables.