In many sciences, it is standard laboratory practice to use a statistical design of experiment and a regression model to study the influence of multiple parameters under a wide range of conditions. The current study aims at investigating the reliability of regression models by examining recently published models. Of particular interest are the assumptions that are not robust to violation such as the reliability of measurements, constant variation of residuals, and sample size. To test regression models simulation is used to model potential measurement error and the importance of sample sizes on parameter estimation. The randomly perturbed designs are then used together with associated mathematical models obtained from the original designs to simulate experiments and obtain new regression models. A comparison of the original model to the new model, and various statistical tests are performed to determine how accurate the original parameters have been predicted when exposed to simulated measurement error.