There are a number of analyses that show relationships between to or more variables. Two of the more common analyses are correlation and regression. Each of them has their place in determine the relationship between two variables (which ideally accurately reflect the concepts to which they are supposed to be assessing).

One of the main differences between correlation and regression is that correlations are bi-directional (i.e., you don't specify which variable predicts which), while regression requires that you determine a predictor and an outcome.

Simple regression examine the predictive relationship between one variable and an outcome, while multiple regression allows you to examine the joint predictive power between multiple (i.e., more than one) predictor and an outcome.

These resources are offered to help you start to understand the differences between the two sets of statistics and when you should (and should not) use them to draw conclusions about the relationship between variables and concepts.