Quantitative+Methods

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AURO UNIVERSITY(INDIA)

The School of Management & Entrepreneurship

Master of Business Administration

Module

Quantitative Methods

Semester-1 (2013-2015)

Module Leader

Chitrakalpa Sen

www.aurouniversity.edu.in


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ENTREPRENEURSHIP

Module Introduction

In todays dynamic and competitive business environment, a key ability for a successful manager

is to use a knowledge - based framework to analyse and make decisions. This course introduces

students to the basic statistical and quantitative models for making informed management

decisions. It focuses on understanding the principles and developing problem-solving skills. This

course is designed to provide students with a sound conceptual understanding of the role that

management science plays in the decision making process. It emphasizes the application of a

wide variety of quantitative techniques to the solution of business and economic problems.

Each week you will be expected to attend lecture, seminar and workshop. All class lecturesand other study materials will be available to you on the Virtual Learning Environment of

Auro University (VLE)

Module Leader: Dr. Chitrakalpa Sen

Tel: 91-261-4088101/201 Extn. 150

e-mail: [email protected]

Contact hours: 24 hours (additional workshops and seminars).

Module Objectives

1. Knowledge and Understanding

Understand the relevant principles of quantitative methods

Understand quantitative analysis of problem solving

Identify and define the problem

Determine the set of alternative solutions

Understand complexities of decision making

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2. Transferable SkillsThis module provides opportunities for students to develop skills of:

Practised Taught Assessed

A. Self Management

i. Manage tasks and time. X

B. Learning Skills

i. Use library skills. X X

ii. Develop independence in

learning.

X

iii. Use a range of academic skills

(analysis, research, synthesis,

evaluation of evidence).

X X X

C. Communication

i. Give clear and effective written

presentation of evidence and

argument.

X X

D. Problem Solving

i. Identify key issues for

investigation.

X X

ii. Construct theoretical frameworks

for analysis of key issues.

X X X

iii. Select optimal

strategies/solutions.

X X X

E. Information Technology

i. Use IT as a resource for

information.

X

Course Prerequisites:

Students are expected to have a basic understanding of mathematics and coordinate geometry.

Assessment structure:

The subject matter covered in this course will be assessed by:

Mid term examination: 20% WEIGHT

Coursework submission: 30% WEIGHT

End term examination: 50% WEIGHT



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A Note on Equal Opportunities

Auro University aims within its courses to provide equal access to learning to students from

diverse backgrounds, irrespective of their gender, race, disability, sexual orientation, age,

religion and maturity. If you feel this module is not fulfilling this aim, please take it up with

the module leader, or ask your student representative to do so. Feel free also to comment on

this aspect of the module in the evaluation.

References:

The Main Text Book: Preferred Text Book for this course would be:

1. Quantitative Methods for Business David R. Anderson, Dennis, J. Sweeney and

Thomas A. Williams, 10th Ed., South-Western.

Alternative Texts:

1. Quantitative Methods : Theory and Applications - J K Sharma, MacMillan India.

Weekly coverage:

S. No. Week Chapter

1 Week I Problem Solving and Decision Making

2 Week II Introduction to probability

3Week III

Probability distributions

4Week IV

Decision analysis, Utility and game theory (MID TERM)

5Week V

Time series analysis

6Week VI

Regression analysis

7Week VII

Introduction to linear programming

8Week VIII

Revision (COURSEWORK SUBMISSION DEADLINE)

Using unfair means in assessments

All assessments are intended to determine your individual skills, abilities, understanding and

knowledge. Cheating is defined as obtaining an unfair academic advantage and any of you

found using any form of cheating, attempting to cheat or assisting someone else to cheat may

be subject to disciplinary action in accordance with the AUs Disciplinary Procedure. The

university takes this issue very seriously and you may be expelled or have your degreewithheld for cheating in assessments. If you are having difficulty with your work it is



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important to seek help from your tutor rather than be tempted to use unfair means to gain

marks. Do not risk losing your degree and all the work you have done. AU defines a number

of different forms of cheating, although any form of cheating is strictly forbidden. These are:

Submitting other people's work as your own - either with or without their knowledge.

This includes copying in examinations; using notes or unauthorised materials inexaminations

Impersonation - taking an assessment on behalf of or pretending to be another student,

or allowing another person to take an assessment on your behalf or pretend to be you

Plagiarism - taking or using another person's thoughts, writings or inventions as your

own. To avoid plagiarism you must make sure that quotations, from whatever source,

are clearly identified and attributed at the point where they occur in the text of your

work by using one of the standard conventions for referencing. It is not enough just to

list sources in a bibliography at the end of your essay or dissertation if you do notacknowledge the actual quotations in the text. Neither is it acceptable to change some

of the words or the order of sentences if, by failing to acknowledge the source

properly, you give the impression that it is your own work

Collusion - except where written instructions specify that work for assessment may be

produced jointly and submitted as the work of more than one student, you must not

collude with others to produce a piece of work jointly, copy or share another student's

work or lend your work to another student in the reasonable knowledge that some or

all of it will be copied

Duplication - submitting work for assessment that is the same as, or broadly similar to,

work submitted earlier for academic credit, without acknowledgement of the previous

submission

Falsification - the invention of data, its alteration, its copying from any other source,

or otherwise obtaining it by unfair means, or inventing quotations and/or references.

Lecture Outline 1

Problem Solving and Decision Making

Overview:

Introduction to quantitative analysis

Quantitative analysis and decision making

Steps in quantitative analysis



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Learning objectives:

To understand the need of quantitative approach for effective decision making

To understand the characteristics and various stages of scientific study

Recognize, classify and use various models for solving a problem.

Workshop: Applications in real life scenario

Seminar: Numerical Problem solving session

Lecture Outline 2

Introduction to Probability

Overview:

Concept of probability

Three methods of assigning probabilities

Introduction to Venn diagram

Some basic laws of probability

Conditional probability

Bayes theorem

Learning objective :

Help yourself understand the amount of uncertainty that is involved before making

important decisions.

Understand fundamentals of probability and various probability rules that help you to

measure uncertainty. Perform several analysis with respect to business decision involving uncertainty.



Lecture Outline 3

Probability Distributions

Overview:



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Random variables

Binomial probability distribution

Poisson probability distribution

Normal probability distribution


Find the mean and variance of a discrete probability distribution

Distinguish between discrete and continuous random variables

Find the mean and variance of a continuous probability distribution

Using probability distributions to solve real-life problems



Lecture Outline 4

Decision Analysis, utility and game theory

Overview:

Concept of utility

Utility and decision making

Introduction to game theory

Pure and mixed strategy


Understand the concept of expected utility

Define the basics of a game

Evaluate conflict dynamics from the standpoint of the self-interests of the Players

Appraise theoretical predictions obtained from Game Theory analyses against realworld conflicts



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Formulate strategic alternatives which take into account the actions of others

(commonly known as a Nash Equilibrium)

Recognize the classic Prisoners' Dilemma

Solve mixed strategy games

MID TERM EXAMINATION

Lecture Outline 5

Time Series Analysis

Overview:

Introduction to time series

Components of a time series

Smoothing methods

Trend and seasonal components

Learning objective:

Understand the central ideas of time series analysis and forecasting

Follow academic literature in applied economics using time series analysis

use time series analysis to test economic theory empirically



Lecture Outline 6

Regression Analysis

Overview:

Introduction to regression

Simple regression

Multiple regression

Learning objective:

Understand how to use regression to analyze a real-life problem



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Learning how to run regression in a statistical software

Interpreting the result



Lecture Outline 7

Introduction to Linear Programming

Overview:

Introduction to linear programming Optimization problem

Graphical solution

Data Envelopment Analysis

Learning objective:

Formulate a combinatorial optimization problem efficiently

Explain the mathematical theory underlying the solution methods.

Analyze the solution to a linear optimization problem Understanding the basic concept of a data envelopment analysis



Lecture Outline 8

REVISION

Assessment Details

End Term Examination

One Midterm - 20%

One coursework submission 30%



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Final examination - 50%

The midterm will take place at the end of week 4 and the topics will be cumulative till week 3.

It will be of 100 marks.

The coursework submission will be prepared by the students in groups. Each group will have

three members and they will have to prepare a project using regression model. 15% score will be

given to presentation and 15% on the merit of the project. Each workshop and seminar will

require the students to form groups among themselves. Each group will have no more than 5

students and no less than 2 students. The groups will be determined by the programme office

and are not subjected to any changes.

The coursework will be based on your learning from the second half of the course, especially on

time series analysis and regression. You will be required to run a regression analysis on data of

your choice and present your findings in form of an academic paper.

The examination, of three hours duration, will take the form of a set of six questions whichranges across topics covered in lectures, seminars and self-study exercises.

The examination mark will be determined by the number of questions answered correctly and the

range of questions has been designed such that pass can be obtained by demonstrating a basic

understanding of key concepts whilst higher marks can be obtained by demonstrating ability to

analyse, evaluate and interpret economic and business data.

Student Assessment of Module

I hope that you found this module challenging but worthwhile. Each year, the module changesslightly, in part as I think of ways I can do things better and in part because students suggest

possible improvements. I would appreciate your comments on what I got right and what I could

improve on. I would therefore be grateful if you could anonymously complete the following

table, adding any additional observations you may have on the reverse. A summary of student

appraisals is reported at Field Meetings, attended by student representatives, and I would be

happy to provide a copy to any student who asks for one.

1 = Strongly Agree 2 = Agree 3 = Neither Agree nor Disagree 4 = Disagree

5 = Strongly Disagree

1 2 3 4 5

This module as a whole:

Was well organised

Had an acceptable workload

Gave me confidence in my understanding of

business economics

Was relevant to my main field/s of study



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Was adequately resourced in terms of learning

support material

Was adequately resourced in terms of

accommodation and physical facilities

The LECTURES:

Were interesting

Were clearly delivered

The SEMINARS:

Were interesting

Were clearly delivered


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Quantitative+Methods