chapter 8

An objective of multivariate analysis is to decrease the size of a data base while preserving information.

Frequencies and descriptives are fundamental techniques in synthesis cases.

For an analysis of frequencies we can only use discrete variables.

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.

The cluster technique look for affinities among every individual to gather individuals in groups.

A histogram represents a frequency distribution.

The cluster technique considers that the market is uniform.

Hierarchical clustering is used to obtain segments.

One of the objectives of multivariate analysis is to establish relationships between variables.

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 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.

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.

Hierarchical clustering is a segmentation technique.

CHAID technique provides a segmentation chart for each segment with regards to the dependent variable.

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.

We read all feedback carefully, but we do not reply to all submissions. If you have an issue requiring a response, please use the Help Center.

chapter 8

Choose a study mode

Podcast

Questions and Answers