In most empirical work, the investigator's understanding of the economic process under study is only minimally reflected in the econometric methodology. This paper suggests that in many cases, the construction of a small-scale simulation can prepare the data for regression in a manner which takes cognizance of the theory of the process. Regression is then used to scale the output of the simulation up to observed magnitudes of the variable to be predicted. The simulation has the function of exploring for the nature of the nonlinearities and interactions and thus replaces the usual search for a form which maximizes R2. The simulation may also be helpful where colinear data are a problem. An example is presented in which the effects of wages, unemployment rates, and labor turnover on poverty are studied through a prepared regression. IN THE LAST three decades, regression analysis has become the Procrustean bed into which all economic data are fitted. In the usual empirical paper by an economist, the obligatory theoretical discussion which precedes the description of the regressions generally contributes little more to the empirical methodology than an indication of which variables ought to be included in which equation, what the signs of the coefficients might be expected to be, and whether the regressions should be run in linear or logarithmic form. One reaction to this state of affairs has been the commencement of construction of large systems of microsimulation, notably one at the Urban Institute emphasizing demography and the distribution of income [6], one at the National Bureau of Economic Research on urban problems [4], and one at the University of Maryland featuring money flows [2]. While these big microsimulations are designed to describe the processes of the economy in a more natural way than can be done exclusively by usual regression methods, they tend to take years to build and tend to be unavailable to economists not involved in their building. It is possible, however, to occupy a middle ground between the regression runners and the large-model microsimulators. In many cases, improvement over the usual regression procedures can be gained by a combination of a very simple do-it-yourself simulation model with regression. The simulation model has the function of preparing the data for regression, in the sense of exploring for the nonlinearities and variable interactions inherent in the phenomenon under study. The regression, which uses the output of the simulation as an explanatory variable, has the function of scaling the simulation results up to observed magnitudes of the variable under study.