About This Course

The "Decision-Making Methods using Statistical Methods and Mathematical Modeling" course offers a comprehensive exploration of decision-making techniques grounded in statistical methods and mathematical modeling. Participants will delve into the principles and applications of statistical analysis and mathematical modeling to inform decision-making processes across various domains. Key topics covered in this course include:

• Introduction to Decision-Making Methods : Gain insights into decision-making theory and the role of statistical methods and mathematical modeling in supporting decision-making processes.
• Statistical Analysis Techniques : Learn about essential statistical analysis techniques, including descriptive statistics, hypothesis testing, regression analysis, and multivariate analysis, and their applications in decision-making.
• Probability Theory : Explore probability theory concepts and their applications in decision-making under uncertainty, including probability distributions, Bayesian inference, and decision trees.
• Mathematical Modeling : Examine mathematical modeling approaches, including deterministic models, stochastic models, and optimization techniques, and their use in decision support systems.
• Decision Analysis : Learn about decision analysis methods, such as decision trees, sensitivity analysis, and scenario analysis, for evaluating alternative courses of action and making informed decisions.
• Simulation Modeling : Explore simulation modeling techniques, including discrete-event simulation and Monte Carlo simulation, for analyzing complex systems and decision scenarios.
• Case Studies and Applications : Through case studies and practical examples, participants will apply decision-making methods using statistical methods and mathematical modeling to real-world decision scenarios across different domains.

Through interactive lectures, hands-on exercises, and case studies, participants will develop practical skills in using statistical methods and mathematical modeling to support decision-making processes effectively.