Regression formulations and related concerns involved in dealing with wage discrimination suits involving college faculty are considered. Several models based in hypothesis testing "policy capturing" uses of multiple linear regression are presented, and problems related to the court's view of statistical significance are examined. Multiple linear regression can be used to determine if there is a policy operating in the hiring process, and if so, to capture it and evaluate it in terms of R2 (R to the power of 2). The process is to add variables to the model explaining the decision to hire or not to hire until a high level of R2 is achieved, evaluate the weighting coefficients to determine the emphasis given each variable, and then test for significance using the F test, which is described. In the event that the plaintiff has introduced a regression model that shows gender to be a statistically significant contributor to salary and particularly if the institution is large, so that virtually any element related to salary will have some degree of statistical significance, a useful strategy is to introduce similar models based on more appropriate assumptions about the group to be studied. Regressions by rank or based on members of only those departments that have both males and females are alternative approaches. Another approach is to study the effect that gender has had on salaries over some interval of time. A repeated measures model can help determine whether corrective measures reduced differences in salary that can be attributed to gender for individuals who were in the system over a given period of years. The issues of statistical versus practical significance, denominator degrees of freedom, and future use of a path analytic approach are considered. (SW)