# R2 (R-squared correlation)

R-squared correlation is an important statistical measure which in a regression model represents the proportion of the difference or variance in statistical terms for a dependent variable which can be explained by an independent variable or variables. In short, R-squared correlation determines how well data is fit the regression model or how well the modeled data is fit to observation data. The coefficient of determination, R2, is similar to the correlation coefficient, R. The correlation coefficient formula will tell you how strong of a linear relationship there is between two variables. R-Squared is the square of the correlation coefficient. For the calculation of R-squared you need to calculate Pearson correlation and then square it.

- r = Pearson correlation
- n = number in the given dataset
- x = first variable in the context (or observation data)
- y = second variable (or modeled data)

Paste 2-columns data here (obs vs. sim). In format of excel, text, etc. Separate it with space:

R-squared correlation (R2):