Stevens Scott P., Professor - Mathematical Decision Making, Predictive Models And Optimization. Course Guidebook.pdf

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Topic

Science

& Mathematics

Subtopic

Mathematics

Mathematical Decision

Making:

Predictive Models

and Optimization

Course Guidebook

Professor Scott P. Stevens

James Madison University

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Scott P. Stevens, Ph.D.

Professor of Computer Information Systems

and Business Analytics

James Madison University

rofessor Scott P. Stevens is a Professor

of Computer Information Systems and

Business Analytics at James Madison

University (JMU) in Harrisonburg, Virginia.

In 1979, he received B.S. degrees in both

Mathematics and Physics from The Pennsylvania State University, where

completing his undergraduate work and entering a doctoral program,

Professor Stevens worked for Burroughs Corporation (now Unisys) in the

Advanced Development Organization. Among other projects, he contributed

to a proposal to NASA for the Numerical Aerodynamic Simulation Facility,

a computerized wind tunnel that could be used to test aeronautical designs

without building physical models and to create atmospheric weather models

better than those available at the time.

In 1987, Professor Stevens received his Ph.D. in Mathematics from The

Pennsylvania State University, working under the direction of Torrence

Parsons and, later, George E. Andrews, the world’s leading expert in the

study of integer partitions.

Professor Stevens’s research interests include analytics, combinatorics,

graph theory, game theory, statistics, and the teaching of quantitative

material. In collaboration with his JMU colleagues, he has published articles

on a wide range of topics, including neural network prediction of survival

in blunt-injured trauma patients; the effect of private school competition on

public schools; standards of ethical computer usage in different countries;

automatic data collection in business; the teaching of statistics and linear

programming; and optimization of the purchase, transportation, and

deliverability of natural gas from the Gulf of Mexico. His publications have

appeared in a number of conference proceedings, as well as in the

European

Journal of Operational Research;

the

International Journal of Operations

& Production Management; Political Research Quarterly; Omega: The

International Journal of Management Science; Neural Computing &

Applications; INFORMS Transactions on Education;

and the

Decision

Sciences Journal of Innovative Education.

Corning Incorporated, C&P Telephone, and Globaltec. He is a member of

the Institute for Operations Research and the Management Sciences and the

Alpha Kappa Psi business fraternity.

Professor Stevens’s primary professional focus since joining JMU in 1985

has been his deep commitment to excellence in teaching. He was the 1999

recipient of the Carl Harter Distinguished Teacher Award, JMU’s highest

teaching award. He also has been recognized as an outstanding teacher

its M.B.A. program. His teaching interests are wide and include analytics,

statistics, game theory, physics, calculus, and the history of science. Much

of his recent research focuses on the more effective delivery of mathematical

concepts to students.

Professor Stevens’s previous Great Course is

Games People Play: Game

Theory in Life, Business, and Beyond

Table of Contents

INTRODUCTION

Professor Biography ............................................................................i

Course Scope .....................................................................................1

LECTURE GUIDES

LECTURE 1

The Operations Research Superhighway...........................................4

LECTURE 2

Forecasting with Simple Linear Regression .....................................13

LECTURE 3

Nonlinear Trends and Multiple Regression.......................................24

LECTURE 4

Time Series Forecasting ...................................................................31

LECTURE 5

Data Mining—Exploration and Prediction .........................................40

LECTURE 6

.............................................49

LECTURE 7

Optimization—Goals, Decisions, and Constraints ............................57

LECTURE 8

Linear Programming and Optimal Network Flow ..............................64

LECTURE 9

Scheduling and Multiperiod Planning ...............................................72

LECTURE 10

Visualizing Solutions to Linear Programs .........................................79

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