Multiple Linear Regression versus Automatic Linear Modelling

Author:

Genç S.1ORCID,Mendeş M.2ORCID

Affiliation:

1. Kırşehir Ahi Evran University, Turkey

2. Canakkale Onsekiz Mart University, Turkey

Abstract

ABSTRACT In this study, performances of Multiple Linear Regression and Automatic Linear Modelling are compared for different sample sizes and number of predictors. A comprehensive Monte Carlo simulation study was carried out for this purpose. Random numbers generated from multivariate normal distribution by using RNMVN function of IMSL library of Microsoft FORTRAN Developer Studio composed the material of this study. Results of the simulation study showed that the sample size and the number of predictors are the main factors that lead to produce different results. Although both methods gave very similar results especially when studied with large sample sizes (n≥100), the Automatic linear modelling is preferred for analyzing data sets due to its simplicity in analyzing data and interpreting the results, ability to present results visually and providing more detailed information especially studying large complex data sets. It will be beneficial to use the Automatic linear modelling especially in analyzing massive and complex data sets for the purposes of investigating the relationships between one continuous dependent and 10 or more predictors and determine the factors that affect the response or target variable. At the same time, it will also be possible to evaluate the effect of each predictor with a more detailed response.

Publisher

FapUNIFESP (SciELO)

Reference14 articles.

1. Discovering statistics using IBM SPSS statistics;FİELD A,2013

2. Evaluating performance and determining optimum sample size for regression tree and automatic linear modeling;GENÇ S.;Arq. Bras. Med. Vet. Zootec.,2021

3. Linear modeling analysis using for determining the factors affecting 305-day milk yield;GENÇ S.;Arq. Bras. Med. Vet. Zootec.,2021

4. IBM SPSS statistics 21 algorithms,2012

5. Applied multivariate data analysis;JOHNSON J.D,1991

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3