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, Mahalanobis’ 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