This chapter covers multiple linear and logistic regression analyses, emphasizing the importance of detecting and addressing multicollinearity. It provides practical examples and detailed explanations on how multicollinearity affects regression models. Diagnostic techniques for identifying multicollinearity and evaluating model fit are discussed, including metrics such as the Sum of Squared Residuals (SSR), standard error of the estimate (SEE), and Akaike Information Criterion (AIC). Throughout the chapter, practical exercises are included to reinforce the theoretical concepts. By the end, readers will have a solid grasp of advanced multivariate analysis techniques and will be proficient in applying these methods to complex datasets for insightful data interpretation.