## OIT 265: Data and Decisions

This is the base version of D&D. This course introduces the fundamental concepts and techniques for analyzing risk and formulating sound decisions in uncertain environments. Approximately half of the course focuses on probability and its application. The remainder of the course examines statistical methods for interpreting and analyzing data including sampling concepts, regression analysis, and hypothesis testing. Applications include inventory management, demand analysis, portfolio analysis, surveys and opinion polls, A/B testing, environmental contamination, online advertising and the role of analytics in business settings more generally. The course emphasizes analytical techniques and concepts that are broadly applicable to business problems.

Terms: Win, Spr
| Units: 3

## OIT 267: Data and Decisions - Accelerated

Data and Decisions - Accelerated is a first-year MBA course in probability, statistics, multiple regression analysis, and decision trees for students with strong quantitative backgrounds. Probability provides the foundation for modeling uncertainties. Statistics provides techniques for interpreting data, permitting managers to use small amounts of information to answer larger questions. Regression analysis provides a method for determining the relationship between a dependent variable and predictor variables. Decision tree analysis consists of quantitative approaches to decision making under uncertainty. Students taking this course need to be comfortable with mathematical notation, algebra, and some calculus. If you are not confident with your quantitative abilities, then you should enroll in
OIT 265. Accelerated D&D will cover material covered in
OIT 265 plus some additional topics such as discrete dependent variable models. While
OIT 267 focuses on real world applicability, we will explore the mathematical underpinnings of these topics in more depth than
OIT 265 as an avenue for deeper understanding. The group regression project is a key component of the course.

Terms: Win
| Units: 3

Instructors:
Yurukoglu, A. (PI)

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