# A Proposal for a New Python Library Implementing Stepwise Procedure
## 5 Conclusions  
The present research has achieved the proposed objectives, contributing significantly to data analyses and statistical modeling in Python. First, we presented the stepwise function based on the statistical significance of the variables in Python, detailed and exemplified, offering researchers, students, and professionals a practical and intuitive tool for carefully selecting variables in multiple regression models. This new tool differs from existing ones, which rely on accuracy metrics, as it is fundamentally based on the statistical significance of the variables. This approach is extremely important in the context of regression models within applied sciences, as failing to exclude variables whose \(\beta\) parameters lack statistical significance can lead to a loss of predictive capability. Such inclusion can alter the magnitude and even the effect of other \(\beta\) parameters in the predictive model. Then, practical examples demonstrated how applying the proposed stepwise function can enhance the reliability of multiple regression models by removing non-significant variables, leading to more accurate and trustworthy model adjustments and predictions. In addition, we presented a comprehensive methodological framework to address common problems in data analyses, such as heteroskedasticity, multicollinearity, and nonadherence of residues to normality, providing a robust and user-friendly computational implementation. The current case study of real estate pricing, considering linear and nonlinear multiple regression models through stepwise, allowed us to consolidate the concepts discussed and validate the effectiveness of the proposed approach. The results showed that the correct application of the stepwise procedure could lead to more accurate models and a better interpretation of the factors that influence real estate pricing. In conclusion, this research fully achieved the proposed objectives, contributing significantly to advancing the understanding and application of statistical methods in Python. The results presented in this paper can be widely used and inspire new debates and contributions in data analysis and statistical modeling. The availability of the stepwise function based on the statistical significance of the variables in Python and the methodological framework will provide practitioners with a more informed and practical approach  
Figure 14: Breusch–[PERSON] test. Figure 13: Application of the Shapiro–Francia test. to handling complex data, driving significant advances in their respective research and application areas. Despite the promising results achieved in this study, it is essential to recognize some limitations that may open space for future research. First, while the stepwise procedure based on the statistical significance of the variables is a valuable approach to variable selection, there may be better options in some scenarios. Additionally, this stepwise process can also be conducted using metrics of fit and accuracy, leaving it to the researcher to decide which method best suits their study. In addition, we focused the case study on real estate pricing, which may limit the generalization of the results to other application areas. Future research could explore the applicability of the proposed stepwise function in different contexts, addressing specific problems and testing the approach's effectiveness in other modeling tasks. Another aspect to consider is that we addressed heteroskedasticity problems, multi-collinearity, and nonadherence of residues to normality in this study. Still, the complexity of these problems can vary widely in different data sets. Future research could explore more sophisticated and specific strategies to address these issues, considering their particularities in different scenarios. Finally, although the methodological framework developed has demonstrated effectiveness in mitigating common problems in data analysis, it is possible to improve its performance and generality through more advanced approaches and adapt to different contexts.