quantatative methods syl

7/31/2019 Quantatative Methods Syl

1/7

BA168IU VNU-International UniversityQuantitative Methods for Business School of Business

THE INTERNATIONAL UNIVERSITY

VNU-HCM

SCHOOL OF BUSINESS

COURSE SYLLABUS

BA168IU

Quantitative Methods for Business

1


2/7


1. COURSE INFORMATION

1

21.1 Course title : Quantitative Methods for Business3

11.2 Units of Credit: 32

1.3 Pre-requisite and/ or Parallel courses: The course presumes a basic understanding of

calculus, probability and statistics.

1.4: Level: Student at 2th year

1.5 Relationship of this course to others

BA168IU Quantitative Methods for Business focuses on theory and applications of

quantitative analysis techniques to managerial decision making. It is useful for all BA

students. Students need to study statistics & probability before study this course.

2. COURSE OBJECTIVES AND LEARNING OUTCOMES

2.1 Course Objectives

This course aims to help students to

Provide students with the methodological understanding of quantitative analysisused in business management.

Create an awareness of quantitative analytical tools used in business management.

Use quantitative analytical tools in business management.

Engage students in critically evaluating the tools of quantitative analysis.

2.2 Student Learning Outcomes

After completing the course, students should be able to:

Quantitative Methods provides students with many quantitative techniques needed toanalyze business situations and make decisions. The course covers game theory, decision

analysis, forecasting, linear programming, goal programming, project management,

simulation, network models and queuing theory.

After completing the course, students should have developed skills in

Understand and use techniques introduced in the course to support decision making.

Think critically about the uses and limitations of quantitative methods in management.

Use computer software for quantitative analysis.

To plan and achieve goals and meet challenges and deadlines.

To learn to work as a collaborative team member.

2


3/7


3. LEARNING ASSESSMENT

3.1 Formal Requirements

In order to pass this course, the students must:

achieve a composite mark of at least 50; and make a satisfactory attempt at all assessment tasks (see below).

3.2 Assessment Details

Mid-Term Exam 30%Assignment 15%

Group Project 15%Final Exam 40%

Total 100%

Mid-term Exam: (Two Hours): open book

Final Exam: (Two Hours): open book

3.3 Marking criteria

Project report

Marking Criteria Marks Learning outcomes/attributesDefining problem 20 Clear, concise statement of the problem.

Ability to structure problems in accordance

with theoretical frameworks to solve them.

Applying model 10 Ability to select or develop a suitable model.

Ability to give compelling arguments andreasoning to support analysis.

Input data 10 Ability to conduct applied research to gather

data/information.

Ability to analyze the data

Developing a solution 20 Ability to get solution by using the computer

and test the solution.Analyzing the results,sensitivity analysis

10 Ability to analyze the results; to get thesolutions with small change in model or

input data.

Presentation of the result 30 Ability to present and give some explanation

of the result and withdraw a lesson about theproblem approach.

3


4/7


3.4 Grading

The letter grade will be followed by the University suggested rule:

90-100: A+, 80-90: A, 70 80: B+, 65 70: B, 55 65: C+, 50 55: C, 30 -


5/7


5. COURSE SCHEDULE

Week Topic Learning materials and

activities1 Introduction to Quantitative Analysis?

What is Quantitative analysis?

The Quantitative analysis approach

How to develop a quantitative analysis model

The Role of Computers in QA approach?

Possible problems in QAImplementation Not just the final step

Text book: Chapter 1

Forming Study groups

Allocation of DiscussionQuestions

How to prepare for project

report


2

Introduction to Game theory

The zero sum game

Mixed strategy

Review Probability concepts and applications (Note:

Review only if lecturers have time There is no examon this chapter. Students studied these concepts in

Business Statistics course)

Fundamental concepts

Random variable

Probability distributions

The Binomial distribution

The Normal distribution

The exponential distribution

The Poison distribution


Assignment (text book)

3 Decision analysis

The six steps in decision making

Types of decision making environment

Decision making under uncertainty

Maximax

Maximin



5


6/7


Criterion of Realism

Equally likely

Minimax Regret

Decision making under risk

Expected Monetary ValueExpected value of perfect information

Expected opportunity loss

Sensitivity analysis

4 Decision analysis (cont.)

Decision tree

How probability values are estimated by Bayesian

analysis

Utility theoryUsing Excel, QM for Window to solve decision theory

problem



5 Review: Regression models(Note: Review

only if lecturers have time-. Students studied these

concepts in Business Statistics course)

Forecasting:Type of forecast

Time series forecast modelsMonitoring and Controlling forecasts

Using the computer to forecast




6 Linear Programming Models: Graphical andComputer methods

Requirements of a linear programming problemFormulating LP problems

Graphicial solution to a LP problem

Four special cases in LP

No feasible solution

Unboundedness

Redundancy

Alternative Optimal solutions

Sensitivity analysis



Midterm

7 Linear Programming Modeling applications

Introduction

Marketing applications

Manufacturing applications

Employee scheduling applications

Financial applications



6


7/7


Transportation applications

Transshipment applications

Ingredient blending applications

8 Linear Programming: the simplex method

How to set up the initial simplex solution

Simplex solution proceduresThe second simplex tableau

Developing the third tableau

Sensitivity analysis with the simplex tableauUsing the computer to solves programming problems.



9 Introduction to Integer Programming, Goal

Programming, Nonlinear Programming

Analytic Hierarchy Process


Analytic Hierarchy

Process Module

10

Project Management

PERT

PERT/cost

Critical path methodProject crashing with CPM

Project crashing with linear programming

Project management with QM for windows



1

1

Waiting lines and Queuing theory Models

(Option)

Waiting line cost

Characteristics of a queuing systemSingle channel queuing model

Multiple channel queuing model

Constant service time model

Finite population model

More complex queuing models and the use ofsimulation

Using QM for windows



1

2

Review

Group presentation

Questions for

Midterm Exam: Game Theory, Decision Analysis (table or tree), Forecasting, LP _Graphic

Final Exam: LP: Models, LP: simplex methods, Analytic Hierarchy Process, Project

Management, Queuing Theory (Option).

7

quantatative methods syl

Documents