# Applied Multivariate Analysis

By Prof. Sugata Sen Roy   |   University of Calcutta
Learners enrolled: 1973
The course will comprise of 33 modules spread over 12 week with the 7th week assigned for the mid-term assessments. The final assessment will be held after the 12th week.
In the course we discuss various multivariate techniques which are basic to the study of multi-dimensional data. These techniques can be applied to diverse fields of natural, biological and social sciences. The course is thus intended for students and researchers who studies involve the simultaneous analysis of several variables
Multivariate methods are primarily of two types. The first is the extension of existing univariate techniques to the multivariate set-up. This brings in more complexity, but the advantage is that the accounting of inter-relationships between the variables leads to better inferences. The second type of studies is unique to multivariate data only. These are either too trivial or the problems are non-existent for univariate data.
In this discourse we will deal with both these types. The Multivariate Linear Models, including multi-response regression, MANOVA and MANCOVA belong to the first category, while the Dimensional Reduction techniques, which include principal component methods and factor analysis techniques, and Clustering and Classification techniques, which include cluster analysis and discriminant analysis belong to the latter.
Of course to fully understand applied multivariate techniques, it is often necessary to have a reasonable knowledge of the theory of multivariate analysis. Part of the course is devoted to an introductory study of basic multivariate theory, particularly those related to the multivariate normal distribution.
Summary
 Course Status : Completed Course Type : Core Duration : 12 weeks Start Date : 15 Jul 2019 End Date : 10 Oct 2019 Exam Date : Category : Mathematics Credit Points : 3 Level : Postgraduate

### Course layout

Week 1
Introduction to Multivariate Analysis, Multivariate Distributions, Multivariate Normal Distribution and Related Results 1
Week 2
Multivariate Normal Distribution and Related Results 2, Multivariate Normal Distribution and Related Results 3, Multivariate Normal Distribution and Related Results 4
Week 3
Classification of Individuals, Cluster Analysis 1, Cluster Analysis 2
Week 4
Cluster Analysis 3, Cluster Analysis 4, Cluster Analysis 5
Week 5
Discriminant Analysis and Classification 1, Discriminant Analysis and Classification 2, Discriminant Analysis and Classification 3
Week 6
Discriminant Analysis and Classification 4, Principal Components Analysis 1, Principal Components Analysis 2
Week 7
Week 8
Principal Components Analysis 3, Principal Components Analysis 4, Principal Components Analysis 5
Week 9
Factor Analysis 1, Factor Analysis 2, Factor Analysis 3
Week 10
Factor Analysis 4, Factor Analysis 5, Canonical Correlations 1
Week 11
Canonical Correlations 2, Multidimensional Scaling, Correspondence Analysis
Week 12
Multivariate Linear Models 1, Multivariate Linear Models 2, Multivariate Linear Models 3

### Instructor bio

I did my B.Sc. with major in Statistics from Presidency College, Kolkata. I then did my M.Sc. in Statistics and subsequently my Ph.D. from the University of Calcutta. I joined the Department of Statistics, University of Calcutta, as Lecturer, in 1989 and am currently serving as Professor and Head in the Department. I have also been a visiting professor in Indian and foreign universities/institutes. My research interests are primarily in the areas of Time Series Analysis, Regression Analysis, Survival Analysis, Development Statistics, Applied Multivariate Analysis and Functional Data Analysis and I have guided research students in these areas. I have also been involved in collaborative work with faculties from other institutes and universities in India and abroad.

### Course certificate

“30 Marks will be allocated for Internal Assessment and 70 Marks will be allocated for external proctored examination”