Regression is a way of describing how one variable, the outcome, is numerically related to predictor (independent) variables. Correlation refers to the interdependence or co-relationship of variables.
•Multiple Linear Regression
Performs simple and multiple regression using least squares. Use this procedure for fitting general least squares models, storing regression statistics and generating prediction and confidence intervals.
•Correlations and Covariances
Calculates the Pearson product moment correlation coefficient between each pair of variables you list.
•Spearman's Rank Correlation
Performs Spearman Rank Correlation.
•Kendall's Rank Correlation
Performs Kendall Rank Correlation.