By the end of this course, learners will be able to define core concepts of Linear Regression, construct simple and multiple regression models, apply dummy variable techniques, and evaluate model performance using statistical tests. Participants will also develop the ability to optimize models through backward elimination and validate predictive accuracy on new datasets.

This course is designed to provide a step-by-step learning pathway from the fundamentals of regression equations to advanced applications in supervised machine learning with R. Learners will gain practical skills by working on real-world datasets, interpreting regression outputs, and visualizing model performance. Unlike theoretical courses, this program emphasizes hands-on practice, allowing participants to strengthen both conceptual understanding and applied expertise. What makes this course unique is its clear progression from basic linear models to advanced optimization methods, ensuring accessibility for beginners while delivering depth for advanced learners. Whether you are a student, analyst, or professional, this course equips you with the knowledge and confidence to apply regression techniques effectively in data-driven decision-making.