Scatter Chart
Scatter Chart
What Is This Report Type?
A Scatter Chart (also called a Scatter Plot or XY Chart) displays data as individual dots positioned on a two-dimensional plane. Each dot represents a single observation, with its horizontal position determined by one variable (X-axis) and its vertical position by another variable (Y-axis). The resulting pattern of dots reveals relationships, correlations, and outliers within the dataset.
Why Is It Used?
Scatter Charts are the primary tool for correlation analysis—identifying whether two variables have a positive, negative, or no relationship. They are essential in statistical analysis, quality control, and scientific research, where understanding the relationship between two continuous variables is critical for decision-making.
Key Features and Characteristics
| Feature | Description |
|---|---|
| Two-Variable Analysis | Plots the relationship between two continuous variables on X and Y axes. |
| Correlation Detection | Visual patterns reveal positive correlation (upward slope), negative correlation (downward slope), or no correlation (random scatter). |
| Outlier Identification | Data points far from the cluster are immediately visible as outliers. |
| Trend / Regression Lines | Optional best-fit lines (linear, polynomial) overlay the data to quantify the correlation. |
| Cluster Recognition | Natural groupings or clusters within the data become visually apparent. |
When to Use It (Use Cases)
- Marketing ROI: Analyzing the relationship between advertising spend and resulting revenue.
- Quality Control: Correlating manufacturing temperature with product defect rates.
- Real Estate: Plotting property size (sq. ft.) against selling price to identify pricing trends.
- HR Analytics: Examining the relationship between employee tenure and performance ratings.
Real-Time Business Example
Scenario: A marketing analyst wants to determine whether increasing ad spend is actually driving more revenue.
Visualization: A Scatter Chart plots each month’s advertising spend on the X-axis and the resulting revenue on the Y-axis. Each dot represents one month. The dots form an upward-sloping pattern from bottom-left to top-right, confirming a strong positive correlation. A linear trend line overlays the data, showing that each additional $1K in ad spend is associated with approximately $3.2K in revenue. Two outlier dots below the trend line indicate months where campaigns underperformed.
Common Metrics Displayed
- Revenue vs. Spend: Correlating investment with return.
- Price vs. Demand: Analyzing how pricing affects purchase volume.
- Time vs. Performance: Plotting task duration against quality scores.
- Size vs. Cost: Comparing product dimensions, team sizes, or project scopes with associated costs.
User Interactions
| Interaction | Behavior |
|---|---|
| Filters | Filter data points by category, date range, or segment to focus the analysis. |
| Hover / Tooltip | Hovering over a dot reveals the exact X and Y values, plus any additional metadata (e.g., record name or date). |
| Click / Drill-Down | Clicking a specific dot navigates to the full detail record for that observation. |
| Zoom / Selection | Drag to select a rectangular region and zoom into a cluster for detailed analysis. |
| Trend Lines | Toggle display of linear, polynomial, or logarithmic regression lines. |
| Export | Export to Excel. |
Creation Steps
- Select Scatter Chart as the report type.
- X-Axis / Y-Axis: Drag two numeric fields for correlation (e.g., Ad Spend and Revenue).
- Group By (Optional): Drag a category field for color-coded grouping.