Program Overview

Applied Statistics and Decision Making (SDGB) Program

The Applied Statistics and Decision Making (SDGB) program offers a range of courses that provide students with a comprehensive understanding of statistical theory, methods, and applications. The program is designed to equip students with the skills and knowledge necessary to make informed decisions in various fields, including business, economics, and social sciences.

Course Descriptions

• SDGB 7811. Applied Statistics Internship (1 to 3 Credits): This experiential elective requires faculty guidance for academic credit from professional training.
• SDGB 7840. Applied Regression Analysis (3 Credits): Focuses on preliminary data analysis, model formulation and estimation, and reliability and sensitivity analysis to understand causal links between various elements of a relationship.
• SDGB 7841. Statistical Theory I (3 Credits): Provides an introduction to mathematical statistics and a foundation for acquiring the skills to apply advanced statistical models to many important areas of decision-making in business.
• SDGB 7842. Statistical Theory II (3 Credits): Builds on Statistical Theory I, focusing on developing an understanding of hypothesis testing, nonparametric statistics, Bayesian statistics, multivariate methods, and linear models.
• SDGB 7843. Judgment and Decision Making (3 Credits): Explores how to rationally approach decision-making and why actual decision-making is often irrational, drawing on contemporary research in economics and psychology.
• SDGB 7844. Stat Methods and Comp I (3 Credits): Introduces statisticians to statistical programming and data analysis, covering topics such as hypothesis testing, regression models, experimental design, and simulation.
• SDGB 7845. Sampling Theory (3 Credits): Provides a foundation in sample design and data collection for decision making, studying theoretical principles and applications.
• SDGB 7846. Advanced Financial Econometric (3 Credits): Covers Bayesian estimation of small-scale financial sector and macro-econometric models, including counter-factual simulations and Monte-carlo methods.
• SDGB 7847. Machine Learning for Stats (3 Credits): Offers a lab-style approach to numerical analysis and optimization methods used to fit statistical models, with an emphasis on implementation and intuition.
• SDGB 7848. Observational Studies (3 Credits): Covers select classes of statistical methods to help analysts design and analyze observational studies for real-world decision-making.
• SDGB 7849. Experimental Design (3 Credits): Examines the design, implementation, and analysis of empirical research methods, including experimental and quasi-experimental designs.
• SDGB 7850. Statistical Risk Analysis (3 Credits): Utilizes tools in probability theory, statistical analysis, decision theory, and cognitive and behavioral sciences to examine various aspects of risk.
• SDGB 7851. Measurement and Data Visualization (3 Credits): Considers how to approach issues in collecting, summarizing, analyzing, and presenting data, including developing appropriate metrics and heuristics.
• SDGB 8999. Applied Stats & Decision (3 Credits): Explores the application of statistical methods to decision-making, with a focus on practical implementation and real-world examples.

Attributes and Prerequisites

• BUAN: Business Analytics
• BUSA: Business Administration
• ISEL: International Studies and Economics in London
• Prerequisites for each course are specified in the course descriptions.