Scatter Plot

Introduction: Scatter Plot

A scatter plot is a statistical chart used to visualise the relationship between two quantitative variables. Each observation is shown as a point on the graph, with one variable on the horizontal axis (X) and the other on the vertical axis (Y).

Background

Scatter plots are among the simplest yet most powerful visual tools for data analysis. In Lean Six Sigma and quality management, they are essential for exploring process relationships, detecting patterns, and identifying potential sources of variation. As part of the 7 Quality Control Tools, scatter plots help teams visually assess correlations before applying more advanced statistical methods.

Key Elements / Features

• Axes: The X-axis represents the independent variable, while the Y-axis represents the dependent variable.
• Data Points: Each plotted point corresponds to one observation in the dataset.
• Patterns: Visual inspection can show positive, negative, or non-linear relationships, as well as clusters or outliers.
• Correlation Strength: The closer the points are to a straight line, the stronger the relationship between the variables.
• Regression Line: A fitted line can be added to quantify the direction and strength of the relationship.

Applications / Examples

• Quality Management: Analysing how process parameters (e.g., temperature, pressure) affect defect rates.
• Lean Six Sigma Projects: Identifying possible cause-and-effect relationships between input (X) and output (Y) variables.
• Business Analytics: Exploring the link between marketing spend and sales growth.
• Research and Science: Studying correlations in experimental or observational data.
  

Example: A production engineer plots machine speed (X) against defect rate (Y) and finds that higher speeds lead to more defects, revealing a trade-off between efficiency and quality.

Relevance / Impact

Scatter plots offer a quick, intuitive way to detect relationships, validate hypotheses, and guide decision-making. They are foundational in process analysis, root cause identification, and continuous improvement, supporting data-driven strategies in Lean and Six Sigma environments.

See also

• 7 Quality Control Tools
• 7 MP Tools
• Process Improvement
• Control Chart