Catalyst Behavior Analyzed via General Regression Model with the Parameters Depending on a Covariate

[Image: see text] In this work, the catalytic activity of modified glassy carbon electrodes with xPd–yLaNi(0.5)Fe(0.5)O(3)–chitosan as an anodic catalyst for the polymeric fuel cell was investigated with cyclic voltammetry and controlled potential coulometry techniques; x and y are the mass loading of noble metal and mixed oxide, respectively. For the first time, the statistical regression mixed models were used to compare the electrocatalytic ability of nanocomposites in a fuel cell. The nonlinear regression model of y(i,j) = f(x(i), (s(j))) + ε(i) was considered and simulated, where X(i) is a random variable, s(j) is a covariate value, ε(i) is a normal random error variable, and θ is a P-dimensional vector of parameters of the mentioned model. A strategy to make a mixed model was proposed by using the maximum likelihood or mean square error methods. Then, the appropriate linear and nonlinear models were applied to the electrochemical results. The equations of current density vs time were obtained via the fitting and simulation of experimental data at different potentials and mass loadings of components. The amounts of transferred charge during the methanol oxidation were calculated vs time through the integration of mentioned equations at different potentials and mass loadings of components.