The simple linear regression model, (1) Y= 0o+ 31X, where Y is the dependent variable, X is the independent variable, and P0 and P1 are population parameters to be estimated empirically, often does not adequately describe or represent the relationship between the two variables. A commonly used procedure to arrive at a more descriptive model is to fit a higher degree polynomial, (2) Y=f3o+1 1X1+32X22+ 03X33 +... +RX,. Since the polynomial regression is simply a multiple linear regression, it can be fitted easily by ordinary least squares techniques, especially with the aid of a digital computer. At times, however, the researcher may believe that his data are better fit by a model where the parameters do not enter linearly. There are several nonlinear regression models in use in biology; what follows is a discussion of one such model, emphasizing the caution which must be exercised in fitting the model by the common approximate method.