Applying the Adjusted Search on t to Search for the Best Regression Model

• Main Authors: Ming-Tang Huang, 黃明堂
• Other Authors: Bor-Wen Cheng
• Format: Others
• Language: zh-TW
• Published: 1999
• Online Access: http://ndltd.ncl.edu.tw/handle/26637335298602668531

碩士 === 國立雲林科技大學 === 工業工程與管理研究所 === 87 === Selecting the best regression model is an important issue in the application of multiple regression analysis. This research combined Backward Elimination with Direct Search on t and established a new searching algorithm for finding the best subset regression model which called Adjusted Search on t, . This reseach used computer program to create three kinds of variable structure datasets, The three kinds of variable structure are: (1) variables that are independent to each; (2) partial variables that are correlated to each; (3) partial variables that appear the chacteristic of multicolinearity. This research compared the results of different searching methods in different variable structures and different numbers of variable. The result of this reseach also shows that: 1. is better than the searching methods of Forward Selection , Backward Elimination , Stepwise Regression and Direct Search on t in different data structures based on the indicators of , P-value and . The characteristics do not change both at six and ten variables . 2. One can obtain better result by applying than applying Direct Search on t in the variable structure with multicollinearity. Keywords: Multiple Regression Analysis ; The Best Subset Regression Model; Variable Selection; Correlation; Multicollinearity.