Applied Multivariate Analysis
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Applied Multivariate Analysis SOLVED PAPERS AND GUESS
Product Details: M D U UNIVERSITY Applied Multivariate Analysis
Format: BOOK
Pub. Date: NEW EDITION APPLICABLE FOR Current EXAM
Publisher: MEHTA SOLUTIONS
Edition Description: 2019-20
RATING OF BOOK: EXCELLENT
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If you find yourself getting fed up and frustrated with other M D U UNIVERSITY book solutions now mehta solutions brings top solutions for M D U UNIVERSITY Applied Multivariate Analysis REPORT book contains previous year solved papers plus faculty important questions and answers specially for M D U UNIVERSITY.questions and answers are specially design specially for M D U 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
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