Syllabus_qant601.pdf

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Quantitative Methods II

QANT 601 A

Course Outline

Instructor’s information

Name:

Dr. Nitin Arora

Office location:

B- Building , 2

nd

Floor

Telephone:

0779965050

E-mail:

[email protected]

Website:

narora.webs.com

Office hours:

M-W : 11:00A.M.-1:00 PM

Tue: 11:00-1:00 / By Appointment

Course information

The use of mathematical models in solving various problems in industry, government and business has grown very rapidly in the last three decades. Today, more and more universities have entire curricula in quantitative methods; and most large firms have departments of operations management, operations research, or management science. A course such as this, which emphasizes the quantitative methods in solving business problems, is an invaluable tool for a business student. The use of quantitative methods in managerial decision making requires that we go through the following four phases, either explicitly or implicitly.

Term and date:

Spring 2011

Course number and section:

QANT 601 , Section A

Credits:

3

Meeting times:

Tuesday: 6:30pm-9:45pm

Building and room number:

As Assigned

Prerequisites and co-requisites:

MIST 595 and QANT 595

Required texts (including ISBN numbers)

Required Textbook:

Reference Textbooks:

Basic Business Statistics: Concepts and Applications, 11th Edition by Berenson,

Levine and Krehbiel, Pearson International Edition, ISBN 13: 978-0-13-500936-9

Course description from catalog

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Course goals and introduction

The use of mathematical models in solving various problems in industry, government and business has grown very rapidly in the last three decades. Today, more and more universities have entire curricula in quantitative methods; and most large firms have departments of operations management, operations research, or management science. A course such as this, which emphasizes the quantitative methods in solving business problems, is an invaluable tool for a business student. The use of quantitative methods in managerial decision making requires that we go through the following four phases, either explicitly or implicitly.

Identification and Definition of the Problem

Throughout this course students will be required to comprehend a case stated in a narrative, and then compile information in such a way that decision variables, uncontrollable variables, measure of effectiveness, and constraints (if any) could be defined explicitly.

2. Formulation or Modeling

Having defined a problem explicitly and exactly, a mathematical model can then be prepared. While preparing a mathematical model, a student may discover that a problem seems to be of a standard kind; e.g. linear programming,

deterministic or probabilistic inventory, etc. If this is the case, the mathematical model should be made to conform exactly to the applicable standard form.

3. Solution

Many of the most frequently used techniques will be described in this course. The solution methodology for solving standard linear programming problems, assignment problems, deterministic and probabilistic inventory problems, decision tree problems, simulation, etc., will be discussed. A detailed list of topics appears later in this course outline.

4. Interpretation and Implementation

Having solved the problem mathematically, the solution is translated back to the real life problem. Every practitioner of management science has to learn “how to communicate” so that other concerned persons in or out of the organization can understand and appreciate the results. A failure to communicate the results effectively is usually a major obstacle in the implementation of the solution.

During this course, a student will be exposed to many cases and problems (both real life and hypothetical), which tend to illustrate each of these phases. Special

emphasis will be given to the definition and formulation aspects, because these tend to teach the student „how to think‟. After learning the concepts, applying the

concepts and setting up the problems, a student will be required to use the computer for solving them.

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probability courses, or has completely forgotten the concepts of algebra and probability, he or she should take equivalent courses before taking this course.

Instructional methods:

a. Instructional methods used in this course include lectures, class discussions, and

in-class demonstrations:

1. Lectures are used to clarify and supplement text readings

2. Class discussions are used to facilitate student understanding and provide integration of course material within the business educational domain 3. Assignments provide hands-on experience with information technologies 4. POM / QM Software Training in the Management Lab.

b. Students are expected to assimilate a portion of course content through

self-study of the textbook and instructor-provided materials.

Learning outcomes and instruments of assessment

Upon successful completion of this course, students will be able to:

The major goal of this course is to instill in the student the increasingly important role of quantitative methods in solving managerial problems. Knowledge of various techniques discussed in this course will assist a current or future manager, not only in decision making, but also in communicating with specialists, such as statisticians, management scientists, systems analysts, etc.

The specific course expected outcomes are five-fold:

1. To achieve an understanding of applied statistical and other quantitative methods

in the solution of managerial decision making problems.

2. To provide enough illustrations of well-known models, so that a student can

identify similar situations that may utilize quantitative methods.

3. To develop a basic proficiency in working with the more frequent decision making

models.

4. To learn the mathematical language and notation, in order to facilitate

communication with specialists.

5. To develop sufficient quantitative preparation for appreciating work done in this

area.

Methods of assessment will include:

1.

Final Term Examination

20%

2.

Mid Term Examination

20%

3.

Homework Assignment

20%

4.

POM/ QM Software Exam

20%

5.

Attendance

10%

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Grading formula

Grade Point Range Interpretation

A 90-100 Excellent

A- 87-89

B+ 84-86

B 80-83 Above average

B- 76-79

C+ 73-75

C 70-72 Average

C- 67-69

D+ 62-66

D 59-61 Below average

F Below 59 Failure

Quantitative Application Software

The CD-ROM included with the book contains a pom / QM 3. It will be used to solve most of the assigned quantitative homework problems. The focus of the course will be on understanding the concepts and then using the software to aide in solving problems.

Description of assignments

They will be a mix of written and POM/QM 3 software based assignments

(8 assignments)

Policy for make-up exams and missed or late assignments

1.

Late homework / project assignment submission will not be accepted.

2.

25% or more unexcused absences will result in an automatic failure (F grade).

3.

Make-up exams / quizzes will not be allowed (except for prior instructor approval

for a documented medical emergency)

4.

Homework assignments and programs are due within a week from the assignment

date, unless the instructor notes otherwise.

5.

All homework assignments are to be typed / submitted on the Turnitin.com

account / if instructed to be sent by email.

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Attendance policy

75% attendance is compulsory

. Regular and punctual attendance is expected of all students. In the case of absence due to emergency (illness, death in the family, accident), religious holiday, or participation in official functions, it is the student's responsibility to confer with the instructor about the absence and missed course work.

Withdrawal policy

A student may withdraw from a course without penalty through the end of the 8th week

of class during a 14- or 15-week semester and through the 8th meeting during an 8week

course cycle. After this, the student must be doing passing work in order to receive a W

grade. Students who are not passing after the 8th week or equivalent will be assigned the

grade of WF.

It is the student’s responsibility to inform the instructor of his/her intention to withdraw

from a course. If a student has stopped attending class without completing all

assignments and/or examinations, failing grades for the missing work may be factored

into the final grade calculation and the instructor for the course may assign the grade of

WF. The grade of F is used for students who have completed the course but whose

quality of work is below the standard for passing.

Withdrawal forms are available in departmental offices and once completed must be filed

with the registrar. Students should be reminded that a W notation could negatively impact

their eligibility for financial aid and/or V.A. benefits, as it may change the student’s

enrollment status (full-time, part-time, less than part-time). International students may

also jeopardize their visa status if they fail to maintain full-time status.

Academic integrity and plagiarism policies

Each student enrolled in a course at NYIT agrees that, by taking such course, he or she

consents to the submission of all required papers for textual similarity review to any

commercial service engaged by NYIT to detect plagiarism. Each student also agrees that

all papers submitted to any such service may be included as source documents in the

service’s database, solely for the purpose of detecting plagiarism of such papers.

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disciplinary charges, pursuant to Article VI, Academic Conduct Proceedings, of the

Student Code of Conduct.

Library Resources

All students can access the NYIT virtual library from both on and off campus at

www.nyit.edu/library

. The same login you use to access NYIT e-mail and NYITConnect

will also give you access to the library’s resources from off campus.

On the upper left side of the library’s home page, select links for “Find Resources”,

“Research Assistance”, “Services”, “Help”, and “About”. Using “Quick Links” on the

right hand side of the home page will also assist you in navigating the library’s web

pages. Should you have any questions, please look under “Research Assistance” to

submit a web-based “Ask-A-Librarian” form.

Support for students with disabilities

NYIT adheres to the requirements of the Americans with Disabilities Act of 1990 and the

rehabilitation Act of 1973, Section 504. The Office of Disability Services actively

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Schedule of Dates

Week

Tentative Topics

1

Intro to Course and Syllabus

Unit 1: Introduction to Mathematical Modeling:

Concept of decision variables, uncontrollable variables, objective

function, and constraints.

· Steps required in modeling.

· Concept of sub-optimization.

· Classification of decision making problems.

Decision making under certainty

- a deterministic inventory problem.

· Decision making under uncertainty.

· Decision making under competition

- a game theory problem.

Read Chapter 1 of Render

2

Unit 2: Inventory Models:

Deterministic inventory models.

4

Unit 3: Linear Programming Models



Setting up the problem



Graphical Method

Read Chapters 7, 8 of Render

5

Unit 3: Linear Programming Models

Using the computer to solve the problem.

·

Dual prices.

·

Maximization, Minimization, Special Cases

·

Network Models.

Read Chapters 9 and 12 of Render

7

Unit 4: Simulation:

· Monte Carlo simulation.

· Flow-charting and bookkeeping.

· Analytical procedures vs. simulation.

· Common applications.

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8

MID TERM EXAMINATION

9

Unit 5: Waiting Lines:

· Characteristics of Queuing Systems

· Single and Multi-Channel models

· Cost Analysis.

10

Unit 6: Regression and Forecasting:

· Regression analysis.

· Correlation analysis.

Read Chapters 4 and 5 of Render and reference from the Berenson,

Levine and Krehbiel statistics book

11

· Modeling and applications.

· Forecasting.

· Time series analysis.

12

· Modeling and applications.

· Forecasting.

Levine and Krehbiel statistics book

13

Unit 8: Modeling and applications.

· Forecasting.

14

Final Term Exam Review cum Software Practice sessions

15

Software Exam

Final Term Exam

I reserve the right to add or delete or modify from this schedule and syllabus with prior

www.nyit.edu/library.