Multivariate Data Analysis

**Topics**

Introduction to Multivariate Data Analysis

Multivariate data, Descriptive Statistics: Array of Data, Mean Vector, Variance Covariance Matrix, Correlation Matrix with the hands-on tutorial

Concept of Distance and its measures

Euclidean Distance, Statistical Distance for uncorrelated Data, Mahalanobisâ€™ Distance for Correlated Data with the hands-on tutorial

Review of Matrix Algebra

Rank of a Matrix, Singularity of a Matrix, Inverse of a Matrix, Orthogonal Matrix, Eigenvalues and Eigenvectors of a Symmetric Matrix, Diagonalization of a Symmetric Matrix with examples

Overview of Univariate Probability Distributions

Multivariate Normal Distribution

Multivariate Random Vector and Linear Transformation, Useful results of Multivariate Normal Distribution

Review of Univariate Hypothesis Testing

Multivariate Hypothesis Testing with examples. The statistical approach to Outlier Detection both in Univariate and Multivariate cases. Test of Multivariate Normality of data

Multivariate Techniques

Cluster Analysis, Principal Component Analysis, Factor Analysis, Discriminant Analysis, Multivariate Analysis of Variance (MANOVA) and Multivariate Control Charts. Forecasting Methods, Introduction to Support Vector Machines, Random Forest as classifiers

Software like R and Minitab will be used for analysis

**Who can attend:** Engineers/ Managers/Scientist/Administrators/Faculties from Universities who are engaged in analyzing the data collected and stored on many variables in the field of design, manufacturing, market survey, service etc. in industry and various other organizations