This course introduces the statistical concepts and methodologies underpinning linear statistical models, with a focus on their application in business analytics. Key topics include multiple regression models, regression models for both quantitative and qualitative variables, model building and variable selection, diagnostics and remedial measures, analysis of variance (ANOVA), logistic regression, time series analysis, and Bayesian linear regression. Students will learn to formulate and test hypotheses, and apply criteria such as Cp, AIC, and BIC for model comparison. The curriculum emphasizes practical skills in diagnosing model issues and implementing corrective measures. Through hands-on projects, students will develop their analytic abilities to integrate and apply quantitative methods to real-world business problems. Additionally, the course will enhance students' proficiency in presenting their analytical findings effectively, preparing them for data-driven decision-making in their professional careers.

Examination 

The final exam is designed to assess students' comprehensive knowledge and ability to apply linear regression techniques to solve business problems. It includes:  

Conceptual questions to test understanding of theories.  

Data analysis problems

requiring the use of statistical software output.  Interpretative questions to evaluate the ability to draw conclusions from analyses.