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Multivariate Data Analysis Questions and Answers Grade A+ 2023 £6.30   Add to cart

Exam (elaborations)

Multivariate Data Analysis Questions and Answers Grade A+ 2023

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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 fas...

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  • May 27, 2023
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  • 2022/2023
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Multivariate Data Analysis Questions
and Answers Grade A+ 2023
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 of MVDA

-Principle Component Analysis/Factor Analysis - -obtain a summary or an overview of a table
identify the dominant patterns in the data such as groups, outliers, trends, and so on

-Principal Component Analysis - -a class of procedures used for data reduction and
summarization

-interdependence technique - -PCA is an _____: no distinction between dependent and
independent variables

-1. to identify underlying dimensions, or factors, that explain the correlations among a set of
variables
2. to identify a new, smaller, set of uncorrelated variables to replace the original set of correlated
variables - -Uses of Factor Analysis

-Bartlett's test of sphericity - -is used to test the hypothesis that the variables are uncorrelated in
the population

-Correlation Matrix - -is a lower triangle matrix showing the simple correlations between all
possible pairs of variables included in the analysis

-1 - -diagonal element are all ___

-Communality - -amount of variance a variable shares with all the other variables

-Eigenvalue - -represents the total variance explained by each factor

-Factor loadings - -correlations between the variables and the factors

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