Importância da regressão linear para análise de dados

Abstract

The objective of this paper is to highlight the importance of linear regression in data analysis, a fundamental statistical technique in several fields of study. Linear regression allows modeling the relationship between dependent and independent variables, facilitating the prediction of results and the identification of trends. Using tools such as the coefficient of determination (R²), Q-Q plots, residual versus predicted value plots, and other diagnostic methods, it is possible to assess the quality of the model's fit and the adequacy of the data, as well as to gain insights from them. The study also explores the practical application of linear regression, demonstrating how this technique can be used to transform data into useful insights. In addition, the paper discusses the limitations and assumptions of linear regression, such as linearity, homoscedasticity, and the independence of errors, since understanding these assumptions is essential for the most consistent application of the technique and to avoid erroneous conclusions.