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TO 501 451 WN 2017
Course Description Applied Business Statistics provides an overview of the frameworks and methodologies for business decision making. The course begins with constrained optimization, which attempts to find the best way to make the most money with limited resources, for instance. These models assume that the analyst knows all that he or she needs to know about the decision problem and are widely used in finance, marketing, operations and logistics. Examples are routing product from producers to consumers at minimum cost, determining product mix to maximize profits, selecting investments to manage cash flows, or allocating advertising budgets to different media. Next, we consider decision making when the outcomes of the decisions are random or not known at the time of making the decision; otherwise known as decision making under uncertainty. For example, future profits depend on future demand, and demand is uncertain, especially for a new product launch. This analysis relies on probability and expectations. Finally, we learn how to use data to reduce some of the uncertainty in the decision problem. Future demand depends on variables such as price, advertising, distribution, product features, and competitive reaction. Statistical analysis uses data to estimate the demand function as a function of these variables. The estimated demand function can be used to find the actions that maximize expected profits. The sensible use of data, combined with managerial judgement, reduces the uncertainty in business decision making, thus leading to better decisions. Topics: 1. Optimization Methods a. Linear Programming b. Integer Programming c. Mixed Programming d. Nonlinear Optimization 2. Decision Making under Uncertainty a. Probability and Expectation b. Maximum Expected Value Criterion c. Descriptive Analysis 3. Using Data to Reduce Uncertainty a. Inference and Hypothesis Testing b. Regression Analysis c. Time Series