Multivariate Analysis

Paper Code: 
STT 332
Credits: 
5
Contact Hours: 
75.00
Max. Marks: 
100.00
Objective: 

This course lays the foundation of Multivariate data analysis. The exposure provided to multivariate data structure, multinomial and multivariate normal distribution, estimation and testing of parameters

Course

Learning outcomes (at course level

Learning and teaching strategies

Assessment Strategies

 

Paper Code

Paper Title

STT322

Multivariate Analysis

(Theory)

 

The students will be able to –

 

CO55: Able to derive various multivariate sampling distributions and use exterior forms where appropriate, to make the necessary changes of variables. Understand and be able to use Kronecker products in problems related to the multivariate normal distribution.

CO56: Understand how the Wishart distribution arises in multivariate sampling and how to use it.

CO57: Understand how to use various multivariate statistical methods (for example: test for significant differences between populations, use principal component analysis and factor analysis, discriminant analysis, cluster analysis and canonical correlation analysis)

Approach in teaching:

Interactive Lectures,

Group Discussion,

Classroom Assignment

Problem Solving Sessions

 

Learning activities for the students:

Assignments

Seminar

Presentation

Subject based  Activities

Classroom Quiz

Assignments

Class Test

Individual Presentation

 

15.00

Multivariate normal distribution and bivariate normal distribution- marginal and conditional distributions, joint distribution of linear function of correlated normal variates. Characteristic function of multivariate normal distribution. Distribution of quadratic forms.

 

 

15.00

Maximum likelihood estimator of the mean vector and covariance, their independence and related distributions. Null and non-null distribution of partial and multiple correlation coefficients. Sample regression co-efficient and its applications.

 

 

15.00

Hotelling-T2 and its properties and applications, Mahanalobis D2. Wishart distributions and its properties. Asymptotic distribution of Z-tanh (r).Multivariate central limit theorem.

 

 

15.00

Classification and discrimination procedure for discrimination between two multivariate normal populations, sample discriminate function, test associated with discriminate functions probabilities of misclassification and their estimation. Classification into more than two multivariate normal population.

 

15.00

Introduction to principle component analysis, Cannonical variables and canonical correlation, factor analysis, cluster analysis, basic methods and applications of MANOVA (without derivation of the distribution of wilk’s)

 

 

Essential Readings: 
  • Giri, N.C. (1977): Multivariate Statistical Inference, Academic Press.
  • Anderson, T .W. (2003): An Introduction to Multivariate Statistical Analysis, 3rd edition John Wiley.
  • Rao, C.R. (1973): Linear Statistical Inference and its Applications , 2nd edition Wiley.
  • Srivastava, M.S. and Khatri, C.G. (1970): An Introduction to Multivariate Statistics, North Holland.
  • Morrison, D.F. (1976): Multivariate Statistical Methods, McGraw- Hill.
  • Nuirhead, R.J.(1982): Aspects of Multivariate Statistical Theory, John Wiley.
  • Kshirsagar, A.M. (1972). Multivariate Analysis, Marshell & Decker.
  • Roy, S.N. (1957): Some Aspects of Multivariate Analysis, John Wiley.
  • Johnson, Richard A., Wichern, Dean W. (2007): Applied Multivariate Statistical Analysis (6th Edition), Pearson.

 

Academic Year: 
2020-2021
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