International Research Journal of Education and Technology

Linear regression is a fundamental aspect of predictive analytics, providing a simple yet robust framework for analyzing the relationship between independent and dependent variables. This study aims to present a thorough examination of linear regression, covering its theoretical foundations and practical applications across various fields. We explore key concepts of linear regression, including model development, parameter estimation methods, and hypothesis testing. Moreover, we investigate more advanced topics like multicollinearity, heteroscedasticity, and model diagnostics, explaining how they influence model accuracy and interpretability. By using examples and case studies, we showcase the versatility of linear regression in real-world situations, from economic predictions to healthcare analysis. Additionally, we delve into modern adaptations of linear regression, such as regularized regression techniques and ensemble methods, emphasizing their effectiveness in managing complex data structures and enhancing model performance. Lastly, we provide guidance on best practices for model selection, validation, and interpretation, enabling professionals to maximize the benefits of linear regression in their predictive modeling projects.

Keywords: Linear regression, Predictive analytics, Model interpretation, Model diagnostics, Regularization techniques, Real-world applications.