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Cluster Sampling Method

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...

Scatter Plot

Scatter Plot

The most common and easiest way to display the relationship between two variables, x and y, is a scatter plot. A scatter plot shows the direction of a relationship between the variables. A clear direction happens when there is either:

Interpreting R Charts

Interpreting R Charts

R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...

Statistical Analysis: Overview

Statistical Analysis: Overview

When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...

Pareto Chart

Pareto Chart

A Pareto chart is a bar graph or a combination of both line and bar graphs. The bar lengths represent the individual values or the frequency, while the lines represent the cumulative total values. In this chart, the longest bars are arranged on the left and the shortest bars on the right, which makes it easier to read and interpret the data. It can also be called a Pareto diagram or Pareto analysis.
The Pareto chart is named after the Italian economist Vilfredo Pareto, who described the Pareto...

Modified Boxplots

Modified Boxplots

A standard box and whisker plot informs us about the spread of the data in a given sample. One can identify the minimum value, maximum value, first quartile value, second quartile or median value, and third quartile.
However, the box plot does not tell the reader about outliers - values that lie far from the center of the data. We can modify the standard box and whisker plot to identify the outliers and visualize the actual spread of the data in a sample.
Initially, we calculate the adjusted...

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Related Experiment Video

Updated: May 9, 2025

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

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Prismatic: Interactive Multi-View Cluster Analysis of Concept Stocks.

Wong Kam-Kwai, Yan Luo, Xuanwu Yue

IEEE Transactions on Visualization and Computer Graphics

|May 5, 2025

Summary

This study introduces Prismatic, a visual analytics system for financial cluster analysis. Prismatic helps investors identify investment opportunities and manage risks by integrating quantitative and qualitative data for better business correlation insights.

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Area of Science:

Computer Science
Data Visualization
Financial Analysis

Background:

Financial cluster analysis aids investors in identifying opportunities and mitigating risks.
Challenges include dynamic correlations, numerous comparisons, and integrating business knowledge.

Purpose of the Study:

To introduce Prismatic, a visual analytics system for interactive financial cluster analysis.
To address the challenges in clustering correlated businesses by integrating quantitative and qualitative data.

Main Methods:

Prismatic employs a multi-view clustering approach, combining dynamic cluster generation, knowledge-based exploration, and correlation-based validation.
It integrates historical performance data with business relational knowledge for nuanced similarity assessment.

Main Results:

The system enriches data-driven clusters with knowledge-driven insights, offering a deeper understanding of business correlations.
Case studies on concept stock formulation and expert interviews validated Prismatic's effectiveness.

Conclusions:

Prismatic provides a comprehensive interpretation of quantitative and qualitative financial data.
The visual analytics system enhances the discovery of investment alternatives and risk management strategies.