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Applied Multivariate Analysis

Applied Multivariate Analysis. Instructors: Dr. Amit Mitra and Dr. Sharmishtha Mitra, Department of Mathematics and Statistics, IIT Kanpur. Multivariate analysis is a fundamental concept in applied statistics. In this course, we shall first look at basic concepts of multivariate distributions and study standard multivariate distributions which provide multivariate counterparts of the univariate distributions. Multinomial, multivariate normal, Wishart and Hotelling's T-squared distributions shall be studied in detail. Important applied multivariate data analysis concepts of principal component analysis, profile analysis, multivariate analysis of variance, cluster analysis, discriminant analysis and classification, factor analysis and canonical correlations analysis shall be covered. The theoretical concepts as well as practical data analysis using real life data shall be used to illustrate and study the concepts. (from nptel.ac.in)

Prologue


Lecture 01 - Basic Concepts on Multivariate Distribution
Lecture 02 - Basic Concepts on Multivariate Distribution (cont.)
Lecture 03 - Multivariate Normal Distribution I
Lecture 04 - Multivariate Normal Distribution II
Lecture 05 - Multivariate Normal Distribution III
Lecture 06 - Some Problems on Multivariate Distributions
Lecture 07 - Some Problems on Multivariate Distributions (cont.)
Lecture 08 - Random Sampling from Multivariate Normal Distribution and Wishart Distribution I
Lecture 09 - Random Sampling from Multivariate Normal Distribution and Wishart Distribution II
Lecture 10 - Random Sampling from Multivariate Normal Distribution and Wishart Distribution III
Lecture 11 - Wishart Distribution and its Properties
Lecture 12 - Wishart Distribution and its Properties (cont.)
Lecture 13 - Hotelling's T-squared Distribution and its Applications
Lecture 14 - Hotelling's T-squared Distribution and Various Confidence Intervals and Regions
Lecture 15 - Hotelling's T-squared Distribution and Profile Analysis
Lecture 16 - Profile Analysis
Lecture 17 - Profile Analysis (cont.)
Lecture 18 - Multivariate Analysis of Variance (MANOVA) I
Lecture 19 - Multivariate Analysis of Variance (MANOVA) II
Lecture 20 - Multivariate Analysis of Variance (MANOVA) III
Lecture 21 - MANOVA and Multiple Correlation Coefficient
Lecture 22 - Multiple Correlation Coefficient
Lecture 23 - Principal Component Analysis
Lecture 24 - Principal Component Analysis (cont.)
Lecture 25 - Principal Component Analysis (cont.)
Lecture 26 - Cluster Analysis: Hierarchical Clustering
Lecture 27 - Cluster Analysis: Hierarchical Clustering (cont.)
Lecture 28 - Cluster Analysis: Nonhierarchical Clustering
Lecture 29 - Cluster Analysis Examples
Lecture 30 - Discriminant Analysis and Classification I
Lecture 31 - Discriminant Analysis and Classification II
Lecture 32 - Discriminant Analysis and Classification III
Lecture 33 - Discriminant Analysis and Classification IV
Lecture 34 - Discriminant Analysis and Classification V
Lecture 35 - Discriminant Analysis and Classification VI
Lecture 36 - Discriminant Analysis and Classification VII
Lecture 37 - Factor Analysis
Lecture 38 - Factor Analysis (cont.)
Lecture 39 - Factor Analysis (cont.)
Lecture 40 - Canonical Correlation Analysis I
Lecture 41 - Canonical Correlation Analysis II
Lecture 42 - Canonical Correlation Analysis III
Lecture 43 - Canonical Correlation Analysis IV

References
Applied Multivariate Analysis
Instructors: Dr. Amit Mitra and Dr. Sharmishtha Mitra, Department of Mathematics and Statistics, IIT Kanpur. Multivariate analysis is a fundamental concept in applied statistics.