Regressão múltipla e logística: explorando suas relações e aplicações

Abstract

This thesis explores statistical regression techniques, organized into three main sections. The first section addresses the construction of linear regression using the least squares method, which minimizes discrepancies between observed data and predicted values. The second section delves into Multiple Regression, allowing a broader analysis by considering multiple independent variables. The third section focuses on binary logistic regression, adapted for binary dependent variables and valuable for classification problems. The primary goal is to present the fundamentals of regression analysis, developing analytical skills essential for research and decision-making across various fields.