Business and engineering management decisions are made with limited data set and partial information in uncertain environments. Therefore, statistical data and decision analysis is required to translate raw data into meaningful information that is relevant to business and/or engineering decision-making (e.g., quality control, product sampling, profit maximization, cost minimization, optimal decision strategy, etc.). The objectives of this course are first to give students a practical and an intuitive understanding of probability and statistics. And then to illustrate how to apply probabilistic methods and decision analysis to make rational and statistically optimal management decisions in the face of uncertainty and based on partial information. In order to achieve the objectives in this course we first develop a foundation of probability and statistics and then utilize Microsoft Excel to master some fundamentals of data visualization, decision analysis, statistical inference, estimation, prediction, confidence interval, and hypothesis testing. The focus will always be on intuition rather than mathematical notation. Topics will also be applied in management settings and practical examples will be discussed to build the bridge between theoretical models and the real world.
This course is specifically designed to be widely accessible. An intermediate level knowledge of probability and math is required. In
addition to that, although we will mostly focus on describing data, interpreting results and computing parameters of our decision
framework, we will use Microsoft Excel for data visualization, decision analysis, statistical inference, estimation, prediction,
confidence interval, and hypothesis testing.
Students will be provided with instructions and the required link to purchase access to the interactive online learning platform that is required in this course.