In 1979, Cleland proposed that all kinetic analyses should be conducted using appropriate regression methods [1]. This allows for statistical analyses of kinetic parameters. Knowledge of errors in statistical parameters allows for the selection of the correct kinetic model (e.g., competitive versus noncompetitive inhibition). In reality, the computational capabilities to perform these analyses were not readily available at the time and it was several years before nonlinear least-squares were routinely used. A similar situation exists today. With the emergence of user-friendly computational software, e.g., Mathematica (Wolfram Research, Inc., Mathematica, Version 10.4, Champaign, IL [2016]) and Matlab (The MathWorks, Inc., Natick, Massachusetts, United States), enzyme kinetics, pharmacokinetics (PK), and any other quantitative modeling effort can be approached using numerical methods. We will loosely define numerical methods as methods to solve series of ordinary differential equations (partial differential equations will not be discussed here). As discussed below, numerical methods are particularly useful for analysis of complex kinetic schemes. This was discussed by Segel [2], but sufficient computational capabilities were not routinely available at that time.