A Proposal for a New Python Library Implementing Stepwise Procedure
Carefully selecting variables in problems with large volumes of data are extremely important, as it reduces the complexity of the model, improves the interpretation of the results, and increases computational efficiency, ensuring more accurate and relevant analyses. This paper presents a comprehensi...
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| Format: | Article |
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MDPI AG
2024-11-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/17/11/502 |
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| author | Luiz Paulo Fávero Helder Prado Santos Patrícia Belfiore Alexandre Duarte Igor Pinheiro de Araújo Costa Adilson Vilarinho Terra Miguel Ângelo Lellis Moreira Wilson Tarantin Junior Marcos dos Santos |
| author_facet | Luiz Paulo Fávero Helder Prado Santos Patrícia Belfiore Alexandre Duarte Igor Pinheiro de Araújo Costa Adilson Vilarinho Terra Miguel Ângelo Lellis Moreira Wilson Tarantin Junior Marcos dos Santos |
| author_sort | Luiz Paulo Fávero |
| collection | DOAJ |
| description | Carefully selecting variables in problems with large volumes of data are extremely important, as it reduces the complexity of the model, improves the interpretation of the results, and increases computational efficiency, ensuring more accurate and relevant analyses. This paper presents a comprehensive approach to selecting variables in multiple regression models using the stepwise procedure. As the main contribution of this study, we present the stepwise function implemented in Python to improve the effectiveness of statistical analyses, allowing the intuitive and efficient selection of statistically significant variables. The application of the function is exemplified in a real case study of real estate pricing, validating its effectiveness in improving the fit of regression models. In addition, we presented a methodological framework for treating joint problems in data analysis, such as heteroskedasticity, multicollinearity, and nonadherence of residues to normality. This framework offers a robust computational implementation to mitigate such issues. This study aims to advance the understanding and application of statistical methods in Python, providing valuable tools for researchers, students, and professionals from various areas. |
| format | Article |
| id | doaj-art-17948ddf59014740afcfce4145ac0a23 |
| institution | Kabale University |
| issn | 1999-4893 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-17948ddf59014740afcfce4145ac0a232024-11-26T17:45:25ZengMDPI AGAlgorithms1999-48932024-11-01171150210.3390/a17110502A Proposal for a New Python Library Implementing Stepwise ProcedureLuiz Paulo Fávero0Helder Prado Santos1Patrícia Belfiore2Alexandre Duarte3Igor Pinheiro de Araújo Costa4Adilson Vilarinho Terra5Miguel Ângelo Lellis Moreira6Wilson Tarantin Junior7Marcos dos Santos8Faculty of Economics, Administration, and Accounting, University of Sao Paulo, Sao Paulo 05508-900, BrazilFaculty of Economics, Administration, and Accounting, University of Sao Paulo, Sao Paulo 05508-900, BrazilDepartment of Management Engineering, Federal University of ABC, Sao Bernardo do Campo 09606-045, BrazilPolytechnic School, University of Sao Paulo, Sao Paulo 05508-010, BrazilProduction Engineering Department, Fluminense Federal University, Niteroi 24210-240, BrazilProduction Engineering Department, Fluminense Federal University, Niteroi 24210-240, BrazilProduction Engineering Department, Fluminense Federal University, Niteroi 24210-240, BrazilFaculty of Economics, Administration, and Accounting, University of Sao Paulo, Sao Paulo 05508-900, BrazilSystems and Computing Department, Military Institute of Engineering, Rio de Janeiro 22290-270, BrazilCarefully selecting variables in problems with large volumes of data are extremely important, as it reduces the complexity of the model, improves the interpretation of the results, and increases computational efficiency, ensuring more accurate and relevant analyses. This paper presents a comprehensive approach to selecting variables in multiple regression models using the stepwise procedure. As the main contribution of this study, we present the stepwise function implemented in Python to improve the effectiveness of statistical analyses, allowing the intuitive and efficient selection of statistically significant variables. The application of the function is exemplified in a real case study of real estate pricing, validating its effectiveness in improving the fit of regression models. In addition, we presented a methodological framework for treating joint problems in data analysis, such as heteroskedasticity, multicollinearity, and nonadherence of residues to normality. This framework offers a robust computational implementation to mitigate such issues. This study aims to advance the understanding and application of statistical methods in Python, providing valuable tools for researchers, students, and professionals from various areas.https://www.mdpi.com/1999-4893/17/11/502supervised machine learningstepwise functionPython function |
| spellingShingle | Luiz Paulo Fávero Helder Prado Santos Patrícia Belfiore Alexandre Duarte Igor Pinheiro de Araújo Costa Adilson Vilarinho Terra Miguel Ângelo Lellis Moreira Wilson Tarantin Junior Marcos dos Santos A Proposal for a New Python Library Implementing Stepwise Procedure Algorithms supervised machine learning stepwise function Python function |
| title | A Proposal for a New Python Library Implementing Stepwise Procedure |
| title_full | A Proposal for a New Python Library Implementing Stepwise Procedure |
| title_fullStr | A Proposal for a New Python Library Implementing Stepwise Procedure |
| title_full_unstemmed | A Proposal for a New Python Library Implementing Stepwise Procedure |
| title_short | A Proposal for a New Python Library Implementing Stepwise Procedure |
| title_sort | proposal for a new python library implementing stepwise procedure |
| topic | supervised machine learning stepwise function Python function |
| url | https://www.mdpi.com/1999-4893/17/11/502 |
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