Missing Data Analysis in Repetitious Experiments Using Grey System Theory

• Main Authors: Jun-Ren Hsiao, 蕭君荏
• Other Authors: Lee-Ing Tong
• Format: Others
• Language: zh-TW
• Published: 2002
• Online Access: http://ndltd.ncl.edu.tw/handle/98761491844924171023

碩士 === 國立交通大學 === 工業工程與管理系 === 90 === Design of experiment methods are widely used for product/process improvement in industry. Sometimes missing data observations occurred in the experiments due to mechanical breakdowns, collecting data falsely, etc. Consequently, experiment results with missing data cannot be analyzed with conventional analysis of variance（ANOVA）methods. Since the experimental data are no longer balanced. Various methods for coping with the missing observations were developed. These methods include eliminating missing data, replacing the missing observations by mean, estimating the values of missing data using statistic/neural networks model. However, these approaches require complicated computations or complex statistical assumptions. Although some methods like mean plugging are convenient to perform, these are unreasonable. This study proposes a procedure to deal with missing data from repetitious experiments by employing grey system theory. The proposed procedure is simple and requires no assumptions. Two cases, one traditional experiment and one Taguchi experiment, are illustrated to demonstrate the effectiveness of the proposed procedure.