The term "1D/2D scatter plots" refers to visualization techniques used to represent data points in one or two dimensions. A 1D scatter plot displays data along a single axis (e.g., value distribution), while 2D scatter plots map two variables (X and Y axes) to identify correlations, trends, or clusters. These plots are widely used in statistics, data analytics, and quality management and serve as a foundation for data-driven decisions.
Interactive Visualization: Zooming, filtering, and selecting individual data points for deeper analysis.
Clustering and Grouping: Automatic detection and visualization of data clusters or group memberships.
Trend Lines and Regression Analysis: Adding linear or nonlinear trend lines to evaluate relationships.
Axis and Scaling Options: Customizable axis display (e.g., logarithmic, linear scaling) for different data ranges.
Multidimensional Marking: Using colors, symbol sizes, or shapes to represent additional variables.
Data Import and Export: Support for standard formats (CSV, Excel, JSON) for data integration or export.
Annotation and Commenting: Ability to annotate individual data points or add summary notes.
A quality manager visualizes deviations from a target value along a single axis.
A data scientist analyzes the correlation between advertising spend (X) and revenue (Y) in a scatter plot.
A production manager uses a 2D scatter plot to identify error clusters in manufacturing processes.
A market researcher graphically displays customer preferences based on two criteria (e.g., price sensitivity and brand loyalty).
An analyst enhances a scatter plot with color coding to highlight different customer segments.
