Abstract
This article points out the difficulties being faced in fitting models for experimental data where either a saturation or toxic effect occurs and attempts to overcome these difficulties by proposing new methods. Polynomial response functions were objected due to their unboundedness and having symmetry about the optimum with quadratic form. Inverse polynomial response functions could not be recommended as they require some constraints and have biased estimates of unknown parameters with unknown sampling distributions. Log-linear response functions never give negative predictions and provide adequate representation of the data within its range. Although log-linear response functions are superior to other response functions but not the only alternative. In this work, the data are summarized successfully by employing ordinary polynomial response functions using suitable transformations and named as "Modified polynomial response functions". This method of model building is simple and has the capability-of explaining the maximum variation of data. Key words: inverse polynomial response functions, log-linear response functions, modified polynomial response functions
