This advanced Ph.D. level course delves into the essential principles and methodologies of regression models, catering to students pursuing a doctorate in the field. The curriculum encompasses a comprehensive examination of several core topics, with a focus on fostering a deep understanding of statistical techniques and their practical applications. The course begins with an in-depth exploration of OLS (Ordinary Least Squares) regression, covering finite sample properties, the Gauss-Markov theorem, hypothesis testing, generalized least squares, and large sample theory. Subsequently, students will gain expertise in panel data models, instrumental variable techniques, and the General Method of Moments (GMM) estimation. The curriculum also encompasses Bayesian Inference, facilitating a nuanced understanding of probabilistic modeling and decision-making. As a tentative component, the course may include the study of linear or nonlinear time series models and other selected topics, depending on the interests of the students and the instructor. Overall, this rigorous course aims to equip future scholars with the essential statistical tools needed to excel in the field of Business Analytics. 

Examination Evaluate the student's understanding of essential course concepts and theories, such as regression analysis, Gauss-Markov theorem, and Bayesian inference, through examination questions that require a comprehensive grasp of the subject matter. Assess the student's skills in analyzing and proving regression theorems during the examination. This includes demonstrating an in-depth understanding of concepts and the ability to logically present proof processes. Determine the student's overall mastery and application of the entire course content through a summative examination. This final assessment will measure the student's performance and achievement of all CILOs to ensure a thorough understanding of the subject.