Linear Regression with R: Build & Optimize

Overview

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.

Syllabus

Fundamentals of Linear Regression

This module introduces the foundational concepts of Linear Regression, focusing on how regression equations are formed, how variables relate, and how to build simple models. Learners will explore the basics of regression algorithms, interpret key equations, and practice constructing and visualizing regression lines with training data.

Advanced Regression Techniques and Applications

This module expands regression learning into advanced techniques, including multiple linear regression, dummy variable encoding, model evaluation, and feature selection methods. Learners will apply regression to new datasets, test model generalization, and implement optimization strategies such as backward elimination for improved accuracy.