AMDA-P10

AMDA-P10
PGDM 2010-12: Term-IV

ADVANCED METHODS OF DATA ANALYSIS (AMDA)

Prof. Prahlad Mishra

COURSE OUTLINE

The behavior of variables relating to product, service and financial markets is determined by the joint and simultaneous operation of a number of economic relations. The statistical/mathematical models summarize such relationships amongst the variables in the aforesaid markets. They are used to estimate and forecast the future movements of the variables under study. In such circumstances the manager is confronted with the task of decision making by using several econometric/statistical models which capture the crucial features of the economic sector or system. Moreover, for a decision making process a manager takes a holistic view of the economy/market. This leads to the understanding of simultaneous operation/interaction of the variables which form the basis of multivariate analysis.

Course Objective

The Course viz., Advanced Methods of Data Analysis (AMDA) seeks to sensitize the students on various methods of data analysis especially the multivariate data analysis methods and the uses of the integrated approach of these methods in the analysis of market data. It focuses on the use of multivariate analysis and their applicability in the economy/market with respect to both dependent and interdependent techniques Basic econometric problems in estimation and their possible remedies, use of qualitative variables in the single equation methods under dependence methods along with the application of a few multivariate techniques from the interdependence methods form the contents of a course like this. Emphasis of this course will be more on developing skill to interpret the statistics relating to the different multivariate methods.

Analysis of time series data with the single equation models will focus on estimation and forecasting along with analysis of forecast errors.

The course will be a project-oriented course so as to give the participants an opportunity to use the different techniques to analyze market data and interpret the results, acquaint themselves with the related problems in estimation and their applicability in the realistic situation.

Pedagogy:

The course is divided into two parts i.e. (i) the theoretical concept of the multivariate techniques (both dependence and interdependence) and (ii) the application of the techniques and interpretation of the statistics in the realistic situations. The participants will make use of SPSS and other statistical packages to analyze the data. The theoretical concept relating to the techniques will be dealt in the lecture sessions and the participants shall have to apply the techniques by using data collected by them.

The presentation and discussion sessions will be used to acquaint the participants with the interpretation of different statistics of the multivariate tools for drawing inferences.
The exercises will focus on group work during the term. The groups will collect data for the group exercises and use those in the multivariate techniques and present it in the class for discussion( Secondary Data set in a few cases will be supplied to the groups and / or the groups will be advised to consult relevant sources of secondary data).

The groups will submit a final project report within two weeks from the date of completion of the term. The final report will incorporate all the exercises and the participants will take note of the points made and feedbacks given during the presentation.

Topics to be covered and session plan:

Sl.
Topics
No. of Sessions*

1. 1. A brief Review of the Tools for Data Analysis with special reference to Dependence and Interdependence Methods
1

2. Two variable and Multiple Regressions as tools for Data Analysis (for both cross section and time series data)
2

3. Econometric Problems in estimation & prediction with their possible remedies (in single equation regression models)
3

4. Use of qualitative variables in single equation regression models

I. Dummy dependent and independent variables and their uses in cross section and time series data
2

II. Discriminant Analysis
2

III III Binary Choice Models (Logistic Regression)
2

5. Application of ANOVA
2

6. Exploratory and confirmatory Factor analysis
2

7. Cluster analysis and customer/buyer segmentation
1

8. Integration of dependence and interdependence multivariate methods for data analysis and market segmentation
2

9. Structural equation modeling
1

The allotment of Sessions is tentative and includes presentation of the exercises in the class. In case of necessity presentation will be held in the extra classes.

References:

There are a few books on the techniques of data analysis. The following books will be very useful or understanding the concepts, fitting the data in the models and interpreting the statistics.

1	Multivariate Data Analysis	J.E. Hair Jr. et.al
2	Econometric Methods	J. Johnston
3.	Introduction to Econometrics	C.Dougherty
4.	Basic Econometrics	D.N. Gujarati
5.	Business Research Methods	Cooper & Schindler

Evaluation:

Class Assignment & Presentation 35%
End Term 50%
Final Project 15%
Created By: Debasis Mohanty on 05/03/2011 at 11:27 AM
Category: PGDM-II Doctype: Document

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Sl.	Topics	No. of Sessions*
1.	1. A brief Review of the Tools for Data Analysis with special reference to Dependence and Interdependence Methods	1
2.	Two variable and Multiple Regressions as tools for Data Analysis (for both cross section and time series data)	2
3.	Econometric Problems in estimation & prediction with their possible remedies (in single equation regression models)	3
4.	Use of qualitative variables in single equation regression models
	I. Dummy dependent and independent variables and their uses in cross section and time series data	2
	II. Discriminant Analysis	2
	III III Binary Choice Models (Logistic Regression)	2
5.	Application of ANOVA	2
6.	Exploratory and confirmatory Factor analysis	2
7.	Cluster analysis and customer/buyer segmentation	1
8.	Integration of dependence and interdependence multivariate methods for data analysis and market segmentation	2
9.	Structural equation modeling	1