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Comprehensive Overview of Multivariate Analysis Techniques and Applications

Explore the fundamentals of multivariate analysis, including techniques like multiple regression, discriminant analysis, and structural equation modeling. Understand how these methods classify groups, predict dependent variables, and assess relationships between variables. This chapter provides insights into the roles of conjoint analysis and factor analysis in extracting meaningful factors and understanding behaviors. Learn how to apply these statistical tools to research and data analysis effectively.

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Comprehensive Overview of Multivariate Analysis Techniques and Applications

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  1. Chapter 19 Multivariate Analysis: An Overview

  2. Learning Objectives Understand . . . • How to classify and select multivariate techniques. • That multiple regression predicts a metric dependent variable from a set of metric independent variables. • That discriminant analysis classifies people or objects into categorical groups using several metric predictors.

  3. Learning Objectives Understand . . . • How multivariate analysis of variance assesses the relationship between two or more metric dependent variables and independent classificatory variables. • How structural equation modeling explains causality among constructs that cannot be directly measured.

  4. Learning Objectives Understand . . . • How conjoint analysis assists researchers to discover the most importance attributes and the levels of desirable features. • How principal components analysis extracts uncorrelated factors from an initial set of variables and exploratory factor analysis reduces the number of variables to discover the underlying constructs.

  5. Untukmelihat file lengkapnyasilahkanmenghubungikamidiwww.mb.ipb.ac.id

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