Study guides, Class notes & Summaries

Multivariate Data Analysis - -deals with statistical analysis of observations where there are multiple responses on each observational unit -a single variable - -MDA models reality where each situation, product, or decision involves more than _____ -process the information in a meaningful fashion - -Multivariate Analysis can be used to ___ -1. Consumer and market research 2. Quality control and assurance 3. Process optimization and process control 4. Research and development - -Uses ...

Types of Multivariate Techniques - -1. Principal components and common factor analysis 2. Multiple regression and Multiple correlation 3. Multiple discriminant analysis and logistic regression 4. Canonical correlation analysis 5. Multivaiate analysis of variance covariance 6. Conjoint analysis 7. Cluster analysis 8. Perceptual mapping, AKA Multidimentional scaling 9. Correspondence Analysis 10. Structural equation modeling and confirmatory factor analysis -Factor analysis - -Analyzi...

Types of Multivariate Techniques - -1. Principal components and common factor analysis 2. Multiple regression and Multiple correlation 3. Multiple discriminant analysis and logistic regression 4. Canonical correlation analysis 5. Multivaiate analysis of variance covariance 6. Conjoint analysis 7. Cluster analysis 8. Perceptual mapping, AKA Multidimentional scaling 9. Correspondence Analysis 10. Structural equation modeling and confirmatory factor analysis -Factor analysis - -Analyzin...

Multivariate Data Analysis - -Research that involves three or more, or that is concerned with underlying dimensions among multiple variables, will involve multivariate statistical analysis. -Variate - -A mathematical way in which a set of variables can be represented with one equation. A linear combination of variables, each contributing to the overall meaning of the variate based upon an empirically derived weight. -Dependence techniques - -Explain or predict one or more dependent...

Bootstrapping - -An approach to validating a multivariate model by drawing a large number of sub- samples and estimating models for each subsample. Estimates from all the subsamples are then com- bined, providing not only the "best" estimated coefficients (e.g., means of each estimated coefficient across all the subsample models), but their expected variability and thus their likelihood of differing from zero; that is, are the estimated coefficients statistically different from zero or not? Th...