COLLABORATION OF STATISTICAL METHODS IN SELECTING THE CORRECT MULTIPLE LINEAR REGRESSIONS
- 1 King Abdulaziz University, Saudi Arabia
Copyright: © 2021 Ali Hussein Al-Marshadi. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
This article considers the analysis of Multiple Linear Regressions (MLRs) that are essential statistical method for the analysis of medical data in various fields of medical research like prognostic studies, epidemiological risk factor studies, experimental studies, diagnostic studies and observational studies. An approach is used in this article to select the "true" regression model with different sample sizes. We used the simulation study to evaluate the approach in terms of its ability to identify the "true" model with two options of distance measures: Ward's Minimum Variance Approach and the Single Linkage Approach. The comparison of the two options performed was in terms of their percentage of the number of times that they identify the "true" model. The simulation results indicate that overall, the approach exhibited excellent performance, where the second option providing the best performance for the two sample sizes considered. The primary result of our article is that we recommend using the approach with the second option as a standard procedure to select the "true" model.
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- Multiple Linear Regression
- Information Criteria
- Bootstrap Procedure
- Clustering Procedure