"How do the leaders of the world's most complex organizations make decisions about complicated issues? Not so long ago, when faced with multifaceted problems, these leaders made decisions based on experience, intuition, and no small measure of luck. But now there's a better way: a branch of mathematics called analytics, or operations research. Mathematics and computer science have perfected formerly top-secret techniques for predicting the best possible outcomes when faced with conflicting options. The purpose is simple: to apply quantitative methods to help investors, scientific researchers, and problem solvers of all kinds make better decisions. Mathematical Decision Making: Predictive Models and Optimization introduces you to this fascinating field through mathematical tools and computer techniques that you can use. All you need is a home computer, a spreadsheet program, and this course, which will describe major mathematical techniques, applications, and spreadsheet procedures for basic analytics in 24 information-packed lessons. Guided by award-winning Professor Scott Stevens of James Madison University, you will learn about linear regression, optimization, decision trees, Bayesian analysis, queuing, and a host of other techniques. You'll find that the challenge of analytics is not the math, which is often surprisingly easy, but the wide array of choices of procedures you have at your fingertips. The art of analytics lies in picking the most effective one to apply to your problem, and this is what Professor Stevens walks you through in fascinating detail. Moreover, you'll see how modern spreadsheets take the drudgery out of finding solutions, and they make setting up and visualizing problems simple and straightforward. Mathematical Decision Making: Predictive Models and Optimization is enlightening, entertaining, and supremely useful to professional managers, students, and problem solvers in any field. You'll find that the applications are truly endless! "