Multiple Linear Regression versus Automatic Linear Modelling [Regressão Linear Múltipla versus Modelagem Linear Automática]

Creative Commons License

Genç S., MENDEŞ M.

Arquivo Brasileiro de Medicina Veterinaria e Zootecnia, vol.76, no.1, pp.131-136, 2024 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 76 Issue: 1
  • Publication Date: 2024
  • Doi Number: 10.1590/1678-4162-13071
  • Journal Name: Arquivo Brasileiro de Medicina Veterinaria e Zootecnia
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, CAB Abstracts, EMBASE, Food Science & Technology Abstracts, Veterinary Science Database, Directory of Open Access Journals
  • Page Numbers: pp.131-136
  • Keywords: automatic linear modelling, modelagem linear automática, multiple regression, R2, R2, regressão múltipla, simulation, simulação
  • Çanakkale Onsekiz Mart University Affiliated: Yes


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.