Bootstrap Nonlinear Regression Application in a Design of an Experiment Data for Fewer Sample Size

TL;DR

This paper explores the use of bootstrap nonlinear regression in experimental design with limited data, demonstrating improved accuracy and comparing computational tools R and SAS.

Contribution

It introduces a bootstrap nonlinear regression approach for experimental design with small sample sizes, enhancing model accuracy and verifying design properties graphically.

Findings

Bootstrap nonlinear regression improves approximation accuracy with fewer samples.

Augmented design with desired properties enhances model performance.

Comparison of R and SAS shows differences in computational efficiency.

Abstract

This paper reports on application of bootstrap nonlinear regression method to a design of an experiment dataset with fewer experimental runs. Design with desired properties was augmented and verified using graphical techniques. The augmented design with the desired properties benefited the accuracy of the approximated function used. The computation power of R-language and SAS for computing nonlinear function and bootstrap was also compared.