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Questions and Answers
An objective of multivariate analysis is to decrease the size of a data base while preserving information.
An objective of multivariate analysis is to decrease the size of a data base while preserving information.
True (A)
Frequencies and descriptives are fundamental techniques in synthesis cases.
Frequencies and descriptives are fundamental techniques in synthesis cases.
True (A)
For an analysis of frequencies we can only use discrete variables.
For an analysis of frequencies we can only use discrete variables.
False (B)
The mean is the middle value when a data set is ordered from least to greatest.
is to keep all dimensions intact.
The mean is the middle value when a data set is ordered from least to greatest. is to keep all dimensions intact.
One of the outputs of contingency tables are statistic indicators that detect the
existence of relationship between variables.
One of the outputs of contingency tables are statistic indicators that detect the existence of relationship between variables.
The cluster technique look for affinities among every individual to gather individuals
in groups.
The cluster technique look for affinities among every individual to gather individuals in groups.
A histogram represents a frequency distribution.
A histogram represents a frequency distribution.
The cluster technique considers that the market is uniform.
The cluster technique considers that the market is uniform.
Hierarchical clustering is used to obtain segments.
Hierarchical clustering is used to obtain segments.
One of the objectives of multivariate analysis is to establish relationships between
variables.
One of the objectives of multivariate analysis is to establish relationships between variables.
Contingency tables are not a fundamental technique in synthesis cases
Contingency tables are not a fundamental technique in synthesis cases
In hierarchical clustering when grouping several variables at least one of them must
be formulated as an interval or ratio scale.
In hierarchical clustering when grouping several variables at least one of them must be formulated as an interval or ratio scale.
In K-means clustering we don't’ need to specify the number of clusters to be formed.
In K-means clustering we don't’ need to specify the number of clusters to be formed.
Hierarchical clustering delivers quantitative information and graphs expressing how
the clusters were formed in each stage.
Hierarchical clustering delivers quantitative information and graphs expressing how the clusters were formed in each stage.
CHAID is an algorithm used to identify group of cases (or variables)relatively
homogeneous with regards to certain selected characteristics.
CHAID is an algorithm used to identify group of cases (or variables)relatively homogeneous with regards to certain selected characteristics.
Contingency tables input are discrete or continuous variables (defined by intervals) in
any measurement scale.
Contingency tables input are discrete or continuous variables (defined by intervals) in any measurement scale.
Hierarchical clustering is a segmentation technique.
Hierarchical clustering is a segmentation technique.
CHAID technique provides a segmentation chart for each segment with regards to
the dependent variable.
CHAID technique provides a segmentation chart for each segment with regards to the dependent variable.
Flashcards
Multivariate Analysis
Multivariate Analysis
The process of analyzing and simplifying data with multiple variables (dimensions) while preserving important information.
Dimension Reduction
Dimension Reduction
Reducing the number of variables in a dataset without losing significant information.
Objective of Multivariate Analysis
Objective of Multivariate Analysis
The goal of multivariate analysis is to find patterns and relationships in data with many variables.
Simplifying complex data
Simplifying complex data
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Improved decision-making
Improved decision-making
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Study Notes
Dimensionality Reduction in Multivariate Analysis
- Multivariate analysis often deals with datasets containing numerous variables.
- Many variables might not be independent or necessary for understanding the phenomena being studied.
- Reducing the number of variables while retaining relevant information is crucial for model building and interpretation.
- Dimensionality reduction methods aim to achieve this by transforming the original variables into a smaller set of uncorrelated variables.
- This smaller set of variables often captures the essential aspects of the original data without significant loss of information.
- The goal is to create a representation of the data that is more manageable, interpretable, and efficient for analysis.
- Various techniques exist for achieving this, including Principal Component Analysis (PCA) and other similar methods.
- Effective dimensionality reduction minimizes loss of information inherent in the original data while simplifying the analysis.
- Choosing the right dimensionality reduction technique is important for a successful analysis.
- The optimal number of dimensions depends on the specific application and the nature of the dataset.
- This reduction facilitates visualization, pattern recognition, and model building with fewer variables.
- Methods used for this include selecting a subset of variables for analysis. This process generally involves identifying the most important or influential characteristics in the data.
- This method aims to achieve the best representation of the data with the fewest variables possible.
- A related approach filters out less relevant variables, improving the efficiency of analytical processes.
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