A nonlinear regression model is applied to several sets of enzyme kinetics data, treating the entire regression vector as the parameter of interest. The resulting marginal posterior distributions are ...

The paper is concerned with parametric models for populations of curves; i. e. models of the form $y_{i}(x)=f(\boldsymbol{\theta}_{\mathbf{\mathit{i}}};x)$ + error, i ...

A hybrid fuzzy neural network model enhances prediction accuracy of hardness properties in high-performance concrete, ...

Model building via linear regression models. Method of least squares, theory and practice. Checking for adequacy of a model, examination of residuals, checking outliers. Practical hand on experience ...