The Linear Models and Regression Analysis course provides a thorough foundation in statistical modeling, focusing on the general linear model (GLM), multiple linear regression, and generalized linear models (GLMs). Students will learn key techniques such as least squares estimation (LSE), hypothesis testing, model evaluation, and the handling of issues like multicollinearity, autocorrelation, and model inadequacies. The course covers advanced methods including logistic regression, Poisson regression, and robust regression, as well as tools for variable selection and model diagnostics. Through practical applications and theoretical insights, students will gain the skills to apply regression models effectively in various real-world contexts, addressing both theoretical understanding and practical challenges in statistical analysis.
