Excerpt from the report Introduction: This paper presents two elementary methods for fitting three different nonlinear functions to empirical data by means of simple linear regression. Iterative least squares methods which have been developed for estimating parameters of nonlinear functions sometimes lead to certain difficulties in application. Because this is the case the much simpler methods developed in this handbook are useful tools for application. The relative merits of this approach versus the nonlinear iterative approach are briefly described in the concluding-section. The Spillman, Gompertz, and Pearl-Reed (logistic) functions are considered. The two methods presented for the Pearl-Reed function are already well known and are given first. Then analogous methods are derived for the Spillman and the closely related Gompertz curves; these apparently have not been presented in the literature.