This book serves as a practical guide for statisticians and modelers seeking to fit and interpret non-linear models using computational tools. It emphasizes the utility of 'stable parameter systems' for enhancing the convergence of optimization algorithms, improving the reliability of dispersion matrices and parameter confidence regions, and facilitating the comparison of competing models. The text offers insights into challenges in model fitting, methods for combining fits across datasets, strategies for optimizing data collection to reduce prediction variance, and techniques for programming specific models to manage diverse datasets.

The book is presented as a handbook for practical statisticians and modelers, suggesting it aims to provide actionable techniques for working with non-linear models. The emphasis on 'stable parameter systems' indicates a focus on computational efficiency and reliability in statistical analysis. The inclusion of practical examples and a demonstration of a specific software program implies a hands-on, applied approach to the subject matter.