Chapter 5 is devoted to review the basics of linear regression in the Volterra models context. The least squares method is presented and contextualized in the covariance domain. Centering, normalization, and standardization are introduced as means to enhance the quality of the estimator. Next, performance indicators to measure the quality of the result are presented and a practical regression of real measurements is performed to illustrate the concepts of this chapter. Regularizations such as Ridge regression and LASSO are introduced as ways of overcoming overfitting and attain a sparse solution. Finally, a brief introduction of iterative techniques such as the steepest descent and the least mean squares algorithms are covered.