Coordinating Qualitative Predictor Variables in an Applied Linear Model: Analysis and Application for Applied Sciences

• Published in: Curēus (Palo Alto, CA) Vol. 16; no. 4; p. e59151
• Main Authors: Ahmad, Sr, Wan Muhamad Amir W, Ahmed, Faraz, Adnan, Mohamad N
• Format: Journal Article
• Language: English
• Published: United States Cureus Inc 27.04.2024; Cureus
• Subjects: Age; Datasets; Epidemiology/Public Health; Feature selection; Mean square errors; Neural networks; Regression analysis; Software; Statistical analysis; Statistical methods; Variables; linear regression; bootstrapping; fuzzy regression; hybrid methodology; mlffnn; qualitative predictors
• ISSN: 2168-8184; 2168-8184
• DOI: 10.7759/cureus.59151

Background In applied sciences, statistical models are pivotal for uncovering relationships in complex datasets. The applied linear model establishes associative links between variables. While qualitative predictors are essential, their integration into linear models poses challenges. The dummy variable approach transforms qualitative variables into binary ones for regression analysis. Multilayer Feedforward Neural Networks (MLFFNN) offer validation of regression models, and fuzzy regression offers alternative methods to address the ambiguity of qualitative predictors. This study aims to enhance the integration of qualitative predictors in applied linear models through statistical methodologies. Material and methods This study design involves the transformation of qualitative predictors into dummy variables, the bootstrapping technique to improve the parameter estimates, the Multilayer Feedforward Neural Network, and fuzzy regression. This study uses the programming language R as an analysis tool. Results The multiple linear regression model demonstrates precision and a significant fit (p<0.05), with an R-squared value of 0.95 and mean square error (MSE) of 9.97. Comparing actual and predicted values, fuzzy regression exhibits superior predictability over linear regression. The MLFFNN yields a reduced MSE net of 0.362, indicating enhanced prediction precision for derived models. Conclusion This study presents a precise methodology for integrating qualitative variables into linear regression, supported by the combination of specific statistical methodologies to enhance predictive modeling. By integrating fuzzy linear regression, MLFF neural networks, and bootstrapping, the proposed technique emerges as the most effective approach for modeling and prediction. These findings underscore the efficacy of this method in seamlessly integrating qualitative variables into linear models, ultimately enhancing accuracy and prediction capabilities.