Course Information for ECON650

Business Economics and Forecasting:
Quantitative Decision Technologies

This is a Web-text companion site for the Web-enhanced textbooks:
Management Science   Decision Making   Optimization    Optimization Applications
Uncertain Decisions   Games Theory   Business Forecasting   Excel
Course E-Labs   Modeling Keywords   Financial Keywords   Economics Keywords


I am looking forward to working with you and hope that you will find the course both enjoyable and informative.

Professor Hossein Arsham   

To search the site, try Edit | Find in page [Ctrl + f]. Enter a word or phrase in the dialogue box, e.g. "analytical geometry" or "probability" If the first appearance of the word/phrase is not what you are looking for, try Find Next.

         MENU

  1. Welcome Message
  2. Tutorial Help for This Course
  3. Course Description and the Prerequisites
  4. Course Structure, Ingredients & Learning Objects
  5. Student To Student:
    Your Fellow Students' Opinion and Advice
  6. This Course Relevancy's to Your Other Courses
  7. The School's Mission and the Course Objectives
  8. Learning Style for this Course
  9. What Math and Stat Do I Need for this Course?
  10. Required Textbook, & Computer Package
  11. Course Requirements, Grading Criteria & System
  12. Instructions for Homework Assignment
  13. Homework Assignment to Do Before Each Class Meeting and Sample Tests
  14. Get Involved: Join Discussion/Newsletter Lists
  15. JavaScript E-labs Learning Objects
  16. Computer-assisted Learning: WinQSB Package
  17. Business Keywords and Phrases
  18. Compendium of Web Sites Review
  19. Books’ Review

Tutorial Help for This Course

You may have to seek tutorial help to improve your Geometry, Algebraic computational and Statistical Problem Solving skills from the Academic Resource Center (ARC) at Room AC 113. Professor Yoosef Kkhadem (ykhadem@ubalt.edu), [(410) 837-5385] is the Coordinator at ARC. He is knowledgeable, and has both experienced and patient. We are fortunate he is the tutor for this course.

A Fact: My past students, who utilized this tutorial service throughout the semester, improved their course grade substantially.

Dear Student

Welcome to: Business Economics and Forecasting: Quantitative Decision Technologies for the MBA

I look forward to working with you and hope that you will find the course both enjoyable and informative.

In our increasingly complex world, the tasks of the decision-makers are becoming more challenging every day. The decision-maker must respond quickly to events that take place at an ever-increasing speed. A decision-maker must incorporate an often bewildering array of choices and consequences into his or her decisions.

The Web site for this course was designed and created for you. No one need be ashamed of what he or she does not know or how long it takes to master new information. Learning by the Web-enhanced course material can be self-paced and non-judgmental. Using advantages of this technology to expand learning opportunities is especially crucial because we live in a time when learning is a necessity and no longer a luxury.

At one time, it was sufficient for a firm to produce a quality product. As competition grows in today's market, simply producing a quality product is not sufficient. Today, a firm must produce a quality product at less cost than its competitors and simultaneously manage inventory, warehouse space, procurement requirements, etc. In the future, still greater demands will be placed upon decision-makers.

Many tools and techniques help individuals and organization make better decisions. This course provides decision makers and analysts the tools that provide a logical structure to understand the mathematical techniques to solve formulated (i.e. modeled) problems. The primary tools are Optimization and Statistical Analysis, which provide structure and value in helping define and under-stand a problem. In this course you will learn quantitative methodologies to determine optimal strategic solutions to described problems. Personal Computers allow application of these techniques even in the small business environment. Finally, a clear understanding of a general approach to problem solving enables you to use other applied decision-making and planning techniques in this course.

Since the strategic solution to any problem involves assumptions, it is necessary to determine how much the strategic solution changes when the assumptions change. You learn this by performing "what-if" scenarios or sensitivity analysis.

Preparation for management, whether it is related to technology, business, production, or services, requires knowledge of tools, which aid in determining feasible and optimal policies. In addition to communication and qualitative reasoning skills, enterprises wishing to remain competitively viable in the future, need decision support systems to help them understand the complex interactions between all components of an organization's internal and external system. Such components are found in environmental design, transportation planning and control, facilities management, military mission planning and execution, disaster relief operations, investment management, and manufacturing operations.

An organization, like other organisms, must keep itself in a state of homeostasis--subsystems regulate one another so none of the parts is ahead or behind the system as a whole. This interaction is not trivial; mathematical modeling assists in understanding these fundamental relationships. OR/MS/DS/SS concepts focus on communication of results and recommended action. This helps build a consensus concerning the possible outcomes and recommended action. The decision-maker might incorporate other perspectives of the problem, such as culture, politics, psychology, etc.

This course overviews the major quantitative modeling tools successfully used to model the complex interactions described above. Although not exhaustive, this course provides framework for further study. The following tools will be studied: analytically based solutions to math models, optimization, regression analysis, and forecasting techniques.

Just like you, most of your classmates are employed full time. They are engineers, doctors, lawyers, and other professionals. You and your classmates want to learn the business side of their professions. It is important to learn the language of the managers to overcome communication barriers. For example, engineers will learn how to translate "precision" into extra dollars in earning/saving.

You may like to join any of the following (free of charge) Discussion/Newsletter List:

Upon completion of this course, you may find that it "validates" what you think about making good strategic decisions and causes a peace of mind. The contents of this course will help you to systematize what you already know from your own professional experience.

For my teaching philosophy statements, visit the Web site On Learning & Teaching.

Feel free to contact me via phone, faxes, or email. There is a lot of material to cover, so let's start now!

Course Description and the Prerequisites

Basic and advanced skills of applied regression methodology, optimization, and strategy. Statistical and analytical techniques are applied across traditional business disciplines to develop the technical competencies necessary for managers in today's competitive global business environment.

Prerequisites: The prerequisites are Both, the ECON504, and the OPRE504 (Business Statistics) OR their equivalents. I suggest refreshing the basic topics covered in these two courses during the first week. Thank you.

Course Structure, Ingredients & Learning Objects

Course Structure: Your course materials are divided into eight ordered sections:

(For your weekly homework, visit the Homework Assignment section on this site).

  1. The Foundation of Decision-Making Process: When one talks of "foundations", usually it includes historical, psychological, and logical aspects of the subject.

  2. Quantitative Tools for Solution Algorithms: Our high-school math review, including Linear Algebra and Calculus. For probability refresher visit Different Schools of Probabilities, and Probability Lessons.

  3. Deterministic Modeling: Linear Optimization Course materials in this part will be presented in the context of a production and operations management applications with economics implications of the optimal decision. Our case study is the decision problem of allocating scarce resources among competitive means.

  4. Probabilistic Modeling Tools: Probability and statistics for modeling risky decisions.

  5. Analysis of Risky Decisions: Applications to optimal portfolio selections in investment decision together with its risk assessment are provided.

  6. Unification of Probabilistic and Deterministic Modeling: It presents the theory of the Two-person Zero-sum games with an illustrative linear optimization numerical example. Applications to optimal portfolio selections in investment decision together with its risk assessment are provided.

  7. Business Forecasting and Regression Analysis: In making conscious decisions under uncertainty, we all make forecasts. We may not think that we are forecasting, but our choices will be directed by our anticipation of results of our actions or omissions. This lecture is intended to help you do a better job of anticipating and hence a better job of managing the uncertainty by using effective forecasting techniques.

Course Ingredients: The Course Ingredient Components Include:

  1. A set of Technical Keywords and Phrases,
  2. A Collection of Problem-Solving Methodologies, and
  3. Economics Interpretations, Their Implications and Applications for the Decision-Maker

Learning Objects: There are varieties of sources in helping you to understand the foundation of decision making. Each of the following items provides you with different perspectives on our weekly topics.

  1. Textbook: Your textbook is the main source reading and the exercise before each class meeting.
  2. Lecture Notes: Lecture notes are not your textbook substitute. They are designed to meet your needs, as I perceive while lecturing.
  3. Live Lectures & Handouts: The lectures are the bases of your interactions as a learning process, with your classmates and me.
  4. Computer Assisted Learning: My teaching style deprecates the 'plug the numbers into the software and let the magic box work it out' approach. The software is an effective tool for experimentation in serving your needed "hands on experience" for understanding the economics implication of the concepts for yourself.

I am sure that your careful readings and effective use of the above learning objects, provide various perspectives, create a deep understanding of the topic, together with the wholeness and manifoldness of this course.

Required Textbook, & Computer Package

Required Textbook: Applied Management Science: Modeling, Spreadsheet Analysis, and Communication for Decision Making,
by Lawrence J., Jr., and B. Pasternack
John Wiley & Sons, 2nd edition, 2002, ISBN: 047126332 X.

A copy of the textbook is available in Langsdale Library at Reserved Circulation desk (under call no. B29). You must have a valid student ID with you to use the book.

The text is a reasonably comprehensive text in its treatment of various Management Science methodologies. A good many of the examples in the book are illustrated using Excel add-ins for computation purposes. We will however, make use of WinQSB which is commercial grade stand-alone software that also happens to be reasonably compatible with the text.

With the purchase of this book you will also receive the WinQSB Decision Support Software for MS/OM. The WinQSB is the Windows version of the QSB (Quantitative Systems for Business) software package runs under the CD-ROM Windows. There is no learning-curve for this package, you just need a few minutes to master its useful features.

The WinQSB is available on the UB network: You may use the computer lab located at the basement of the Business Center to do your WinQSB computer homework. To access any WinQSB modules: (1) Click on START, (2) Click on All Programs, (3) Click on WinQSB.

The WinQSB Decision Support Software is also available separately from the John Wiley & Sons publisher, ISBN 0-471-40672-4, 2003.

Your textbook is available at the UB Bookstore, (410) 837-5604.

The textbook chosen for this course is excellent. It is a modern, well-written and clear account of the issues facing decision makers doing business. It is easy to read, has broad coverage and is eminently suitable for self-study with many applications.

Notice that the Topics Web site units contain my weekly lecture notes. The purpose of these units is not to replace your textbook. Rather, its purpose is to provide you with other perspectives on the same topics to enhance your deep understanding.

Recommended Readings: I strongly recommend a reading of the following books:

Linstone H., Decision Making for Technology Executives: Using Multiple Perspectives to Improved Performance, Artech House, SBN: 0890064032, 1999. A copy of this book is also available as "a reserve item" for your use upon request at the Langsdale Library.

Mingers J., and A. Gill, (Eds.), Multimethodology: The Theory and Practice of Integrating Management Science Methodologies, Wiley & Sons, 1997. A copy of this book is also available at the Langsdale Library.

Further Readings: There are some Decision-Making textbooks that you may find helpful. They are located at the Langsdale Library:

Decision-Making Books:

  1. Decision sciences: An Integrative Perspective
    By C. Kunreuther, Paul J.H. Schoemaker
    Cambridge University Press, 1993
    Location: HD30.23 .K46

  2. Management science: An Aid for Managerial Decision Making
    By M. Austin, James R. Burns
    Macmillan Press, 1995
    Location: T56 .A87

  3. Management Science: Decision Making through Systems Thinking
    By Hans G. Daellenbach, Donald C. McNickle
    Macmillan Press, 2005
    Location: T57.6 .D32

  4. Management Science for Decision Makers
    By Larry M. Austin, Parviz Ghandforoush
    West Pub. Co., c1993
    Location: T56 .A88

  5. Management Science for Decision Makers
    By Larry M. Austin, Parviz Ghandforoush
    West Pub. Co., 1993
    Location: T56 .A88

  6. Management Science Knowledge: Its creation, Generalization, and Consolidation
    By Arnold Reisman
    Quorum Books, 1992
    Location: T56 .R542

  7. Operations research: A Fundamental Approach
    By James E. Shamblin, G. T. Stevens, Jr
    McGraw-Hill, 1994
    Location: T57.6 .S48

  8. Operations Research: Applications and Algorithms
    By Wayne L. Winston
    Duxbury Press, 1997
    Location: QA402.5 .W54


This Course Relevancy's to Your Other Courses

Today's business decisions are driven by data. There is an amazing diversity of data in all aspects of our lives and in business. Business managers and decision makers are encouraged to justify their decisions based on data. MBA graduates with strong quantitative skills are in demand. This demand increases as the impetus for model-based decisions strengthens and the amount and accessibility of data increases. The quantitative toolkit is developed and enhanced at all stages of your career.

Every course in the MBA program deals with making good business decisions. Each course seems like a scattered piece of a puzzle. This course brings the pieces together by means of a unified, systematic, focused approach to decision-making, that is, Quantitative Decision Technologies. It is magnificent to recognize the unity of various business concepts in the MBA Program.

ACCT 640: Accounting for Managerial Decisions: This course integrates systems analysis, design and control for general decision-making in accounting systems. These systems use the General Structural Decision-Making Process for performance measures, cost of modeling, evaluation, and interactions among accounting system components.

Management is aware that the value of firms is the ultimate measure of company performance. For example, management has been using at least of the six common accounting measures as an operating guide, however, the linkage between operational planning and value is vague and complex and, therefore, difficult to apply. Managers need to have clear targets and performance measures to track progress.

ECON 640: Global and Domestic Business Environment: In ECON650 we learn the "soft side" of Quantitative Decision Technologies. This is the human side of decision-making, which draws on the philosophy of decision-making, politics and psychology. This perspective of Quantitative Decision Technologies is important for the decision-maker. It is important for a decision-maker to establish goals to implement the decision that best fits the business environment. ECON650 also asks how an organization evaluates itself to make decisions about downsizing (decreasing current structure) and expanding (increasing current structure), organization evaluation and developmental processes.

INSS 640: Information System Technology: Three stages of the decision-making process:

  1. Systems analysis: understanding the problem
  2. Systems Design: searching for a "good strategic solution"
  3. Updating the solution: controlling the dynamic nature of information systems

The general approach to the decision-making process in ECON650 prepares students to understand Information Systems Technology from a broad perspective.

MGMT 640: Strategic Innovation and Renewal: ECON650 covers two types of decision-making: Personal and Public. Factual data and scientific tools are used to make "good, strategically defensible" decisions. This course helps you become a responsible decision maker to the shareholder or stakeholder via objective performance assessment and evaluation processes.

MKTG 640: Organization Creation and Growth: The decision-making process in marketing is about the development of new goods and services and technology. We study a manufacturing company that decides whether to develop a new product. Students learn a structural decision-making process to decide whether to hire a marketing consulting firm, the reliability of the consulting firm recommendation and other components for making a good marketing decision.

Course Requirements, Grading Criteria & System

Course Requirements & Grading Criteria
Readings & homework 20%
Computer assignment 20%
Mid-term examination 30%
Final examination 30%

Grading Syatem
Grade Marks GPA
A 90 and above 4.00
A- 85–89 3.7
B+ 80–84 3.3
B 75–79 3
B- 70–74 2.7
C+ 65-69 2.3
C 60-64 2.0
C- 55–59 1.7
D 50-54 1.3
F 50 AND below 00.00

Weekly Homework: For the chapters to be read and HW assignment visit weekly the Course Information site.

Your assignments will be collected and graded. Your homework assignment consists of two parts:

  1. Reading the lecture notes, and Reading and problem solving from your textbook (100 points).

  2. Computer implementation with commentaries, using the WinQSB, E-labs, or Excel, since without computer package one cannot perform any realistic data-based decision making (100 points).
Homework Due Date: Meeting deadlines and even sending me material before deadlines are very important, since you will be engaging in group learning activities where time is crucial. So you should make every effort to complete your work on time. Therefore, late homework submissions carry no credit at all.

You do not have to type your homework assignments; you may save some times in spending more on learning than typing. In this case please, please hand-in your eligible homework on time.

Course Grading System
90 - 100 80 - 89 70 - 79 65 - 69 60 - 64 otherwise
A
B +
B
C +
C
F


General Instructions

  1. Write everything you know about the topics, one by one.

  2. When you can't think of anything more, give yourself time to look for topics and details you may have missed.

  3. Ask yourself, is there anything else I may have missed? Be as inclusive as possible.

  4. Summarize your writing to create fewer pages.

  5. Re-organize to make even fewer pages.

  6. Ask, How do the topics fit together? What elements are related and how?

  7. Ask, What is the significance for me? What can I do with it? What are the implications?

  8. Go back to step 3, until you have as few pages of summary as possible.

The above process helps to crystallize your mind to be reflective and responsive to questions posed about topics you've learned in this course and reinforces the topics in your mind.

To view a Summary-Sheet for the first test, prepared by one of your classmates, click here. If you think you have prepared a better one, kindly send it to me via an attached email. Thank you.

What Math and Stat Do I Need for this Course?

Don't Panic, high school math (Word.Doc) will suffice! There will be some refreshers.

The following sites will help too:

Our high-school math review, including Linear Algebra and Calculus, Math for Economics, and Calculus. I do recommend refreshing your knowledge about solving systems of equations by visiting Solving System of Equations.

For probability and statistics refresher visit Different Schools of Probabilities, Probability Lessons, and A New View of Statistics.

The probability applications we do use in this course are at Expected Value and Variance, and Multinomial Distributions Web sites.

Homework Assignment to Do Before
Each Class Meeting and Sample Tests

Notice:
This part of the Course Information is updated weekly; therefore before doing your homework please make sure re-visiting this part.

Please read and follow the Instructions for your homework assignment. Thank you.

We will proceed in the following sequence (Not a weekly-schedule of topics).

  1. Overview of the Course: Read my Welcome Message on this page. Read the Preface, the Management Science Modeling (Ch. 1). Read also the introductory sections of all the chapters (i.e., sections 1 of all chapters) in your textbook. This is a good way to get a grip on your textbook and the concepts contained therein.
    Visit Success Science and Leadership Decision Making by going over the title of the topics therein. Visit the following Web sites too:
    INFORMS
    Inform's Resources
    OR/MS Resources

    After you did your reading assignment, then write a 2-page essay (format-free) entitled: "What is Applied Management Science?" Your essay should, among others, address some of the following questions:



  2. Analytical Geometry Review: Read Ch. 2, the Lecture Notes on Linear Programming (1-7), and then Formulate (do not solve) Problem 2.7 (i.e., Chapter 2, Problem 7, Wilson Manufacturing Decision, on page 103) as a linear program (LP).
    I do recommend refreshing your knowledge about solving systems of equations by visiting the Web sit Solving System of Equations by Elimination.

    Review and then perform some experiments on the following two topics, then write a short report for each: Compound Interest Analysis
    Break-Even Point Analysis

  3. Linear Programming (LP): Graphical & Algebraic Solution Algorithms, Read Linear Optimization handout. Read Ch. 2 and the course lecture notes (1-7). Do all parts of problem 2.7, with a detailed step-by-step description of the graphical method, using graph paper (Word.Doc , graph paper (PDF). This part of assignment, makes one conscious about what one does.

  4. Computer Implementation and Sensitivity Analysis: Solve problem 2.7 by the Algebraic Method and compare the results with your graphical solution to identify the one-to-one relationship between these two different solution algorithms.
    Read The Dual Problem: Its Construction and Economics Implications handout. Review the Sensitivity Analysis section of the course lecture notes. Apply the right-hand-side (RHS) value and coefficients of the objective function (known as the cost coefficients, because historically during World War II, the first LP problem was a cost minimization problem) sensitivity range to problem 2.7 in Ch. 2, computer implementation together with managerial and economics interpretations of the computer solution. Construct the dual problem, solve it and then provide economical interpretations for the dual and its solution. To construct the dual of a given problem by using WinQSB's Linear and Integer Programming module, click on Format, then select "Switch to the Dual Form".

    The Algebraic Method involves solving many linear systems of equations. When the LP problem has many variables and constraints then solving so many systems of equations by hand become very tedious and even for very large-scale problems it is an impossible task. Therefore, we need the computer to do the computations for us. One of the algorithmic and computerized approaches is The Simplex Method, which is an efficient and effective implementation of the Algebraic Method. There are well over 400 LP solvers, all of which using the Simplex method, including your software. Upon solving the LP problem by computer packages, the optimal solution provides valuable information, such as sensitivity analysis ranges.

    As you know by now, this course has three ingredients: A set of Technical Keywords and Phrases, A Collection of Problem-Solving Algorithms, and Managerial and Economics Interpretations, and the most important of all their Implications and Applications to Business Decision-Making. As I pointed out this course is not about say, linear programming (LP), we are using LP as an application and as a tool. Since you have mastered, the Keywords & Phrase, and Techniques, now we are able to concentrate on the Managerial Business Decision-Making Process. The lecture note section on Managerial and Economics Interpretations of the WinQSB Combined Report deals with how to interpret and describe the computational results in computer output such as, the optimal strategic solution, sensitivity ranges, shadow prices, and other useful information for the decision-maker.

    Perform some "what-if" scenarios analysis on Problem 2.7. That is, use your computer software package to do some numerical experimentation on variations of Problem 2.7. Again, this computer-assisted learning assignment provides a "hands-on" experience, which will enhance your understanding of the technical concepts, involved in various topics of controlling the problems, which we have covered. Your experimentation must include for example, Adding a New Constraint; Deleting a Constraint; Introducing a new product; and Terminating a product. This computer-assisted learning concepts provides a "hands-on" experience which will enhance your understanding of the technical concepts involved in various topics of sensitivity analysis that we have covered.

  5. Integer LP Applications, Formulation and Solution Read What Can Go Wrong in Integer Programming? and Chs. 2, and 3. Formulate problems 2.33 and 2.34, and use the WinQSB package to find the optimal solutions with some managerial explanation on the output for each problem.

    Check your formulations with those on page 628-629 before using your software.

  6. Transportation and Assignment Decisions and Other Network Problems: Read Ch. 4, and the lecture notes. Solve at least any two of the following problems 4.1, 4.2, 4.6, 4.7, and 4.9 by implementing them on WinQSB's Network Modeling module, provide your managerial and economics interpretations of the optimal solution for each problem.
    You may ask what are the Managerial and Economics Interpretations?

    Managerial and Economics Interpretations: The decision problem is stated by the decision-maker often in some non-technical terms. When you think over the problem, and finding out what module of the software to use, you will use the software to get the solution. The solution should also be presented to the decision-maker in the same style of language, which is understandable, by the decision-maker. Therefore, just do not give me the printout of the software. You must also provide managerial and economics interpretations of the solution in some non-technical terms.

  7. Decision Analysis and Probabilistic Models: Read Ch. 6 and the course lecture notes. Do problems 6.1 - 2, 6.3 - 5, and 6.34 by hand and WinQSB's Decision Analysis module applications.

    How stable is your decision? The computer packages such as your WinSQB, are necessary a very helpful tools for the decision maker in performing stability and sensitivity aspects of the decision whenever there is uncertainty in the payoffs and or in assigning probabilities in any decision analysis.

    As a cautious note, you may experience some difficulties in comprehending the decision analysis problems, this is true for everyone while translating the way the problems are worded and the type of questions that are asked. Therefore, the most difficult part of decision analysis is the translation of the problem. Here are my suggestions: Read the problem may time, slowly. I suggest also drawing a decision tree to start with, then read the problem few time to modify the tree. Remember that, the mathematical representation of a decision analysis problem is the decision tree.

    Probability Refresher: Read A Review of Statistics and Probability for Business Decision Making under Risk handout.

    JavaScript for Statistical Decision Making
    JavaScript for Decision Analysis

  8. Review Session and Preparation for First Examination: What have we learned up to now? Walking through Past First Exam (Word.Doc). Modeling and LP Practice (Word.Doc).

    Preparation for the next week test: Your preparation is a very important undertaking in terms of integrating what you have learned each week in order to see the whole picture and inter-connectivity of the topics.

    To prepare yourself for the actual test, you are advised to review all the topics we have covered, to review past homework assignment, and then prepare your own few pages of a summary sheet. The process of producing a summary sheet, helps you to crystallize your mind to be reflective and responsive to any question posed to you about the topics you've learned in this course, it also helps you to reinforce the wholeness of the topics in your mind.

    To view a Summary-Sheets prepared by one of your classmates, click LP/ILP Summary Sheet.

    To view a Summary-Sheets on Decision Analysis, click here.

    If you think you have prepared a better Summary-Sheets, kindly send it to me via an attached email. Thank you.

    Exercise Your Knowledge on this Past First Exams:

    Past First Exams (Word.Doc)

  9. First Examination: November 14, 2006. Read the Examination Facts. The main purpose of taking the examinations is to find out how reflective your mind is in answering a set of questions correctly. The objective is to maximize the number of correct solutions, subject to a limited time constraint (a 2-hours session).

  10. Business Forecasting and Linear Regression: Read Ch. 7, do problems 7.1-7.4, 7.6-7.8 by hand and/or WinQSB's Forecasting and Linear Regression module applications (if available).
    Read An Overview of Forecasting. Read Predictions by Regression. Read the journal article Seasonal and Cyclic Forecasting in Small Firm. Read the course lecture notes forecasting parts.

  11. Review and Preparation for Final Examination. Read the Examination Facts.

    Exercise Your Knowledge on this Past Final Exam:

    Past Final Exam (Word.Doc)

  12. Final Examination (December 12, 2006) is a comprehensive one. Read the Examination Facts.

         Instructions for Homework Assignment

  1. Weekly homework will be assigned and graded by the instructor.

  2. The readings from the textbook will be supplemented by this course Web site materials.

  3. Students must seek tutorial help from the Academic Resource Center at 410-837-5385, Email, located at AC 111.

  4. Students should attempt as many of the problems in each chapter as possible. At least one problem representative of each topic covered in the classroom should be attempted. Suggested problems would include those for which solutions are available in the back of your textbook. Problem formulation and solving are an important aspect of learning statistics. It is therefore important that you regularly do your homework assignment selected from the text.

  5. The use of a scientific calculator is required for the course and should be brought to each class meeting.

  6. Keep a copy of your complete homework and any other material before submitting for grading. Keep the copy until you receive a grade notification form me. These steps will ensure the safety of any material that is lost or unduly delayed. If some material are delayed or lost, i.e., not received on time, you will be ask to resubmit another copy. In the unlikely event that you are unable to resubmit another copy, you will be required to redo it.

  7. All students are expected to follow the Academic Honor Code of UB.
    "Academic honesty is based on the principle that one's work is one's own. The University of Baltimore Academic Integrity Policy encourages all members of the University to accept responsibility for taking academic honesty seriously by being well-informed, by contributing to a climate in which honesty is valued, and by considering responsible ways to discourage dishonesty in the work of others. Students, faculty, administrators, and staff should not condone or tolerate cheating, plagiarism, or falsification, since such activity negatively affects all members of the academic community." Academic Integrity Policy and Procedures. Student Handbook: 2, II.B., 1994.

Learning Style For This Course

This course requires a particular learning style known as learning-to-learn. Effective and efficient learning includes completing weekly homework assignment and learning from feedback. Knowledge conquered by thinking for yourself becomes a possession -- a property entirely our own.

Unfortunately, most classroom courses are not learning systems. Instructors attempt to help their students acquire skills and knowledge with lectures, tests and memorization. Instructors "tell," which doesn't translate into usable skills. We learn by doing, failing and practicing. Computer assisted learning serves this purpose.

The change in learning in this course over the years is less emphasis on strategic solution algorithms and more on modeling processes, applications and software. This trend continues as more students with diverse backgrounds seek MBA degrees without too much theory and mathematics. Our approach is middle-of-the-road: no excess of math or software. We learn how to formulate problems prior to software usage. You should learn how to model a decision problem, first by hand and then by using software. The software should be used for two purposes:

  1. Computer-assisted learning concepts and techniques, and

  2. For large problems that are too difficult to solve by hand.

What are the most critical challenges in learning for this course?

  1. To refresh your high school math including linear algebra, basic statistics, and probability. Also a review of your Calculus.

  2. To learn the new technology, mainly the use of software within a reasonable amount of time. The learning curve of the software we will be using is very sharp.

  3. To link the course materials with other courses in your MBA program.

  4. What is Quantitative Decision Technologies? A rational, structured approach to problem solving. It is the study of developing procedures that are used in decision-making and planning. An objective measure of performance must be identified to measure success. The objective must represent the decision-makers goal and serve as a starting point for developing a model for the problem.

  5. The context of modeling: What is a quantitative decision model? There are two types of models: Deterministic and Probabilistic. Deterministic modeling is linear programming for optimization while decision analysis is a probabilistic modeling tool used for problems under uncertainty.

  6. Model design, selection and setup: This includes justifying model selection (validation), setting assumptions, parameters, advantages and limitations of various models, considering the effects of data quality and accessibility, regulations, implicit versus explicit assumptions, computer models (verification), and sensitivity analysis.

  7. Input data selection and analysis: How to find data and the balance between quality, accessibility, credibility, and relevance. How to evaluate data quality and understand its impact.

  8. Analysis of results: How to tell if results are reasonable, sensitivity of output to changes in input, recognition of the useful life of a model.

  9. Cases and applications: Word Problem Formulation, Linear Programs, Transportation Problem, Assignment Problem, Shortest Path Problem, Max Flow Problem, Critical Path Method in Project Management, Decision Making under Risk, Forecasting Techniques.

  10. Communication: Clearly and accurately communicate the process and result by understanding the nature of the audience, effects of standards and regulations, use of appropriate format and media and maintenance of internal documentation.

It is axiomatic that if learning occurs, there is change in you. Change might occur in your attitude, thinking, beliefs and/or behavior. Something will have changed or else learning simply did not occur. I am sure you will be enthusiastic about the topics covered in this course throughout the semester and beyond. Enthusiasm is one of the most powerful engines of success; enthusiasm changes problems into challenges. When you study for this course, put your whole mind into it. Stamp your work with your own personality when it is submitting. Be active, energetic, honest, and remember: learning-to-learn was never achieved without enthusiasm.

The School's Mission and the Course Objectives

Merrick School of Business Mission Statement: Our mission is to prepare our diverse mix of students in collaboration with the business community to succeed in a dynamic global economy. The goal is to make excellence accessible. We achieve our mission by:

Course Objectives: What Do I Learn?

An efficient and effective learning begins with asking yourself How to Study? I would like to insist that most parts of this course require a particular learning style. The effective and efficient learning style for this course is doing your homework assignments on a regular weekly basis and learning from your mistakes whenever I provide feedbacks.

I am sure you will be enthusiastic about the topics covered in this course throughout the semester and beyond. Enthusiasm is one of the most powerful engines of success. When you do study for this course, do it with all your might. Put your whole mind into it during the semester. Stamp your work with your own personality when submitting them to me. Be active, be energetic, be enthusiastic and honest, and you will accomplish the objectives of this course. Remember that, learning-to-learn was never achieved without enthusiasm.

The general objective of the course is to assist you in understanding and applying the general process of structural decision-making and its components.

Notice that, although we have multiple overall objectives for this course, this does not make our task a "multiple-objective problem", since there is no maximization nor minimization statement in any of our objectives.

Upon completing this course successfully, you should be able:

  1. To understand the nature of statistical inference; that is, its scope and limitations and its proper role in the process of scientific investigation.
  2. To be able to express a generally posed scientific question as a statistical question.
  3. To be familiar with a variety of commonly used techniques and the models underlying them.
  4. To be able to recognize the nature of, and to model, the random variation underlying given a data set.
  5. To recognize the important characteristics, keywords, phrases, and concepts statistical data analysis (i.e., professional identity).
  6. To be able to use statistical packages to perform statistical calculations.
  7. To be equipped with a variety of techniques for condensing statistical data, in preparing to make inferences about a population based on a sample from it.
  8. To be able to decide how to obtain a suitable random sample from the entire population.
  9. To understand the role that statistical data analysis plays in managerial decision making under risk.
  10. To explain an analytical model for structuring and analysis of business decision problem.
  11. To discuss the complimentary nature of the rational and behavioral approaches to decision-making.
  12. To Discuss the usefulness and the limitations of quantitative decision technologies.
  13. To use sensitivity analysis to gain insights of the optimal decision making in response to the changes in the decision-maker's environment.
  14. To understand and apply the general process of structural decision-making and its components to solve their business problem.
  15. To apply Quantitative Decision Technologies to case studies to find solutions to real life business problems including those in global environment.
  16. To communicate effectively the analysis and results of a business decision problem to the decision-maker.
  17. To discuss the ethical dimensions by addressing integrity issues in data collection and consideration of the human-side of modeling process.
  18. To learn the concepts and techniques of Quantitative Decision Technologies by doing weekly homework assignments and learning from my feedback.
  19. To learn and apply new technologies, including commercial software packages, to aid business decision-making and planning, and to obtain timely solutions to decision problems.
  20. To understand how to apply forecasting methods in a real situation: choosing the best approach and the best forecasts.
  21. To appreciate the mechanics of making forecasts: time series and causal modeling.
  22. To be able to discuss business time series, identification of the hidden behavior, selection of a best model to forecast the future.

JavaScript E-labs Learning Objects

This section is a part of the JavaScript E-labs learning technologies for decision making.

Each JavaScript in this collection is deigned to assisting you in performing numerical experimentation, for at least a couple of hours as students do in, e.g. Physics labs. These leaning objects are your statistics e-labs. These serve as learning tools for a deeper understanding of the fundamental statistical concepts and techniques, by asking "what-if" questions.

Decision Making in Economics and Finance:

Probabilistic Modeling: Time Series Analysis for Forecasting

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