We also calculate the R-squared value to measure the correlation between two variables. The value of R-squared can range from 0 to 1, which explains the degree to which your input variables explain the variation of your output / predicted variable. So, if R-squared is 0.8, it means 80% of the variation in the output variable is explained by the input variables. the higher the R-squared, the better the model fits your data.
In simple terms, it represents how much of a change in one variable causes a change in another.
For example, if you have two variables (say X and Y) and you know that they are strongly correlated then it would be reasonable to assume that most of the variation in X can be predicted by knowing Y.