A statistical criterion for the estimation of the goodness of fit in correlation analysis. Least squares methods aim to minimize the sum of squared differences between the observations and the predictions from a model. For example, virtual population analysis (VPA) tuning often involves finding the estimates of abundance and mortality rates that minimize the sum of squared differences between standardized catch per unit effort (CPUE) and predicted abundance from the VPA.