Abstract

In design of experiments, setting exact replicates of factor settings enables estimation of pure-error; a model-independent estimate of experimental error useful in communicating inherent system noise and testing model lack-of-fit. Often in practice, the factor levels for replicates are precisely measured rather than precisely set, resulting in near-replicates. This can result in inflated estimates of pure-error due to uncompensated set-point variation. In this article, we review previous strategies for estimating pure-error from near-replicates and propose a simple alternative. We derive key analytical properties and investigate them via simulation. Finally, we illustrate the new approach with an application.

摘要

在实验设计中，设置因子设置的精确重复可以估计纯误差；与模型无关的实验误差估计，可用于交流固有系统噪声和测试模型失拟。在实践中，重复的因子水平通常是精确测量的，而不是精确设置的，从而导致近似重复。由于未补偿的设定点变化，这可能导致对纯误差的夸大估计。在本文中，我们回顾了以前从近似重复中估计纯错误的策略，并提出了一个简单的替代方案。我们推导出关键的分析特性并通过模拟对其进行研究。最后，我们通过一个应用程序来说明新方法。