This course focuses on various statistical techniques and their applications that will enable students to perform and explain statistical data analysis to make sound decisions and critically evaluate statistical reports or findings. You will learn to think critically about how others use statistics and how it impacts your day-to-day life and career. Methods of inference involving two independent samples and paired data are presented. The analysis of variance is examined for single-factor and multi-factor experiments. Regression analysis for simple linear models is discussed, followed by non-linear and multiple regression models.
This course introduces probability models and statistical methods most likely to be encountered and used by students in engineering and the natural sciences careers. This introduction will emphasize, from the outset, that variation is the source from which all statistical methodology flows. The discussion includes the practical aspects of data collection and description statistics with an introduction to the basic concepts of Probability Theory and probability distributions, correlation, point estimation, confidence intervals, and hypothesis tests.
Decision support systems (DSS) compile raw data from various inputs, organize the data, and help decision-makers analyze the data and generate meaningful insights that can solve their problems and make better decisions. This course, a complete online class, guides the design of decision support systems for industries and service systems based on operations research models. Includes spreadsheets, databases, and integrated software development environments to implement decision support systems. This course covers methods, tools, and techniques for designing the functional aspects of enhanced decision-making and developing DSS interfaces.
Discrete event simulation (DES) simulates the behavior and performance of a real-life process, facility, or system. DES models help to depict the behavior of a complex system as a series of well-defined and ordered events and work well in virtually any process where there is variability, constrained or limited resources, or complex system interactions. Students will investigate using discrete-event simulation to solve mathematically intractable problems in stochastic modeling. The course emphasizes the fundamental concepts of and proper interpretation of results from discrete-event simulation models.
Management in all industries is moving toward more objective decision-making; the healthcare industry, however, has lagged many other industries in this respect. This course in operations analysis emphasizes applying quantitative techniques to problem-solving and decision-making related to the management of healthcare providers, including, but not limited to, hospitals, physician group practices, health maintenance organizations, and nursing homes. This course will be taught from the perspectives of decision-making and control systems in general and their applications in healthcare provider management. The course will emphasize learning various concepts and techniques and apply the techniques to diverse decision-making contexts.