Applied Multivariate Analysis SOLVED PAPERS AND GUESS

Product Details: Rohtak   UNIVERSITY Applied Multivariate Analysis

Format: BOOK

Pub. Date: NEW EDITION APPLICABLE FOR Current EXAM

Publisher: MEHTA SOLUTIONS

Edition Description: 2021-22

RATING OF BOOK: EXCELLENT

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If you find yourself getting fed up and frustrated with other Rohtak   UNIVERSITY book solutions now mehta solutions brings top solutions for Rohtak   UNIVERSITY Applied Multivariate Analysis REPORT book contains previous year solved papers plus faculty important questions and answers specially for Rohtak   UNIVERSITY .questions and answers are specially design specially for Rohtak   UNIVERSITY students .

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Applied Multivariate Analysis

UNIT-I
Multivariate Analysis: Concept, the variate, Measurement scales, Measurement error, Methodology of Model Building. Multivariate Analysis of Variance: One independent variable at two levels and one dependent variable, two-group MANOVA, Multiple-group MANOVA, MANOVA for two independent variables or factors. Repeated Measure Analysis of Variance: Between-subject and within-subject factors and designs, univariate and multivariate approaches to repeated measure analysis.
UNIT-II
Principal Components Analysis: Geometry of principal components analysis, analytical approach, issues relating to the use of principal components analysis, use of principal components scores. Factor Analysis: Basic concepts and terminology of factor, objectives of factor analysis, geometric view of factor analysis, factor analysis techniques-principal components factoring (PCF), principal axis factoring, and factor analysis versus principal components analysis, factor rotation, and factor scores.
UNIT-III
Discriminant Analysis: Geometric view, analytical approach, classification methods, Fisher's linear discriminant, Mahalanobis distance. Canonical Correlation: Geometry of canonical correlation, analytical approach, canonical variates and the canonical correlation, statistical significance tests for the canonical correlations, interpretation of the canonical variates, practical significance of the canonical correlation.
Cluster Analysis: Hierarchical clustering, Non-hierarchical Clustering