OMG355 Syllabus - Multivariate Data Analysis - 2021 Regulation - Open Elective | Anna University

OMG355 Syllabus - Multivariate Data Analysis - 2021 Regulation - Open Elective | Anna University

OMG355

MULTIVARIATE DATA ANALYSIS

 L T P C
3003

OBJECTIVE:
• To know various multivariate data analysis techniques for business research.

UNIT I

INTRODUCTION

9
Uni-variate, Bi-variate and Multi-variate techniques – Classification of multivariate techniques – Guidelines for multivariate analysis and interpretation.

UNIT II

PREPARING FOR MULTIVARIATE ANALYSIS

9
Conceptualization of research model with variables, collection of data –-Approaches for dealing with missing data – Testing the assumptions of multivariate analysis.


UNIT III

MULTIPLE LINEAR REGRESSION ANALYSIS, FACTOR ANALYSIS

9
Multiple Linear Regression Analysis – Inferences from the estimated regression function – Validation of the model. -Approaches to factor analysis – interpretation of results.

UNIT IV

LATENT VARIABLE TECHNIQUES

9
Confirmatory Factor Analysis, Structural equation modelling, Mediation models, Moderation models, Longitudinal studies.

UNIT V

ADVANCED MULTIVARIATE TECHNIQUES

9
Multiple Discriminant Analysis, Logistic Regression, Cluster Analysis, Conjoint Analysis, multidimensional scaling

TOTAL: 45 PERIODS

OUTCOMES:
• Demonstrate a sophisticated understanding of the concepts and methods; know the exact scopes and possible limitations of each method; and show capability of using multivariate techniques to provide constructive guidance in decision making.
• Use advanced techniques to conduct thorough and insightful analysis, and interpret the results correctly with detailed and useful information.
• Show substantial understanding of the real problems; conduct deep analysis using correct methods; and draw reasonable conclusions with sufficient explanation and elaboration.
• Write an insightful and well-organized report for a real-world case study, including thoughtful and convincing details.
• Make better business decisions by using advanced techniques in data analytics. ‘

REFERENCES:
1. Joseph F Hair, Rolph E Anderson, Ronald L. Tatham & William C. Black, Multivariate Data Analysis, Pearson Education, New Delhi, 2005.
2. Barbara G. Tabachnick, Linda S.Fidell, Using Multivariate Statistics, 6th Edition, Pearson, 2012.
3. Richard A Johnson and Dean W.Wichern, Applied Multivariate Statistical Analysis, Prentice Hall, New Delhi, 2005.
4. David R Anderson, Dennis J Seveency, and Thomas A Williams, Statistics for Business and Economics, Thompson, Singapore, 2002
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