Statistical Modelling

This is an advanced course in Statistical Methods that aims at describing some advanced level topics in this area of research with a very strong potential of applications. This course also prepares students for undertaking research in the area of Mechanistic nonlinear growth models.


  • Empirical and mechanistic models. Nonlinear growth models, Like monomolecular, Logistic, Gompertz, Richards. Applications in agriculture and fisheries.
  • Formulation of nonlinear statistical model. Estimation of parameters using iterative procedures, Like Taylor’s , Steepest descent, Levenberg - Marquardt’s. Choice of initial values. Examination of residuals and adequacy of a model. Fitting of nonlinear statistical models using nonlinear estimation procedures and software packages.
  • Applications in plant growth and animal physiology. Two-species systems. Lotka-Volterra, Leslie-Gower and Holling-Tanner non-linear prey-predator models. Volterra’s principle and its applications. Gause competition model. Multi-species modeling.       
  • Compartmental modeling - First and second order input-output systems, Dynamics of a multivariable system.


Fitting of mechanistic nonlinear models, Application of Schaefer and Fox nonlinear models, Fitting of compartmental models.