Scale-up Suite Help
Fitting exercise produces a poor fit

A common reason for a poor fit is a mismatch between your model and the experimental data you have provided.  The source of this mismatch may be either the model or the data.  Check basic items such as:

Beyond these basic aspects, the quality of your fit can be assessed in many ways. Two of the most common and useful are:

Visual inspection: The default Data versus Model view in Fitting will help your eye to judge whether there is good correspondence between model and reality.  Even better, running the model with the updated parameter values in Simulator gives a good indication of the quality of the fit (i.e. do the lines go near/ through the datapoints). This is an important first check of the fit, and shows any obvious problems (e.g. flat profiles of species etc., which can occur at a local or 'false' minimum).

Review the Fitting results tab and Fitting Report: Even if the fit looks good visually, it is advisable to check the numbers in the Fitting results display and the Fitting Report. In particular:

Check for large confidence intervals on parameters. This means a wide range of parameter values can fit the data reasonably well and there is no 'sharp minimum'. Large confidence intervals can imply:

Check the user-defined or assumed errors on the individual datapoints, as reported in the fitting report.  Compare these to the measured values of responses and judge whether they are reasonable. 



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