Regression Analysis: Linear, Polynomial, and Logistic Models

Regression analysis is the backbone of predictive modeling, allowing us to uncover hidden relationships within data and make informed future predictions. Understanding when and how to apply different regression models is a critical skill for any aspiring data professional. This text-only course guides you from foundational statistical concepts to implementing and evaluating key regression models. You will gain the confidence to analyze data relationships and choose the right modeling approach for your specific analytical goals. What you will learn: Understand the fundamental mathematical and statistical concepts behind regression analysis; Implement linear regression models to predict continuous numerical outcomes; Apply polynomial regression to capture non-linear relationships in complex datasets; Build logistic regression models to solve binary classification and probability problems; Evaluate model performance using modern metrics like R-squared, Mean Squared Error, and accuracy; Identify and mitigate common modeling issues such as overfitting and multicollinearity. You will start by exploring essential terminology and the mathematical foundations of regression before moving step-by-step through practical modeling scenarios, code-based examples, and performance tuning strategies. This course is designed for absolute beginners in data analysis, statistics, or machine learning, requiring no prior modeling experience. Start reading today to master the core regression techniques used by data professionals worldwide.