Correlation and regression are statistical methods that help us determine interactions of variables. Both are being used in statistical analysis of basic and clinical research. Correlation (r) is a measure of linear relationship between two numerical measurements made on the same set of subjects and it is represented by correlation coefficient. Values of correlation coefficient range between -1 and 1. Pearson's and Spearman's coefficients of correlation are the most often used correlation coefficients. Correlation can be linear and non-linear. We calculate the significance of correlation (P) in an effort to determine significance of correlation coefficient. Regression is a statistical method that allows us to predict values of one variable from another. The simplest regression is linear regression. The success of regression equation is valued by analysis of residuals. Multiple regression is used to predict one variable from several known variables.