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Plot (x, y) data pairs on a scatter chart to find correlations and clusters. Download as PNG.
A scatter plot places each data point at coordinates defined by two numeric variables. This makes it the right chart when you want to understand the relationship between those two variables rather than show a trend over time or compare category sizes. Ad spend vs revenue, temperature vs energy consumption, and customer age vs purchase value are all relationships that scatter plots reveal at a glance.
Scatter plots are also the best tool for spotting outliers. A point far from the main cluster stands out immediately on a scatter chart in a way that would be invisible on a bar chart or invisible in a table. Outliers in business data are often the most interesting observations: unusually high-performing campaigns, unusually long customer lifetimes, or data entry errors worth correcting.
When your data has two variables and you want to understand whether they move together, independently, or in opposite directions, a scatter plot is the appropriate starting point. It shows the full distribution of two-variable data without collapsing it into averages or summary statistics.
Each row in the table represents one data point. Enter the x value in the left column and the y value in the right column. The axis labels can be set to describe what each variable represents, for example "Ad Spend ($)" on the x-axis and "Revenue ($)" on the y-axis.
The chart renders all points immediately as you type. Add as many rows as your dataset requires, then inspect the pattern before downloading. The distribution of points often reveals the shape of the relationship before any statistical analysis is applied.
Click "Download PNG" when the chart is ready. The export is at 2x resolution, keeping dots and axis labels sharp in slides and documents. No data leaves your browser.
An upward slope from left to right, where higher x values tend to pair with higher y values, indicates a positive correlation. Revenue generally increases as ad spend increases; height generally increases with age during childhood. The steeper and tighter the upward band of points, the stronger the positive relationship.
A downward slope indicates a negative correlation: as x increases, y tends to decrease. Customer churn rate vs product usage time is a common example. A cloud of points with no discernible slope suggests the two variables have no meaningful linear relationship, though a non-linear relationship could still exist.
Points that sit away from the main cluster are outliers. They might represent exceptional cases, data quality issues, or genuinely different sub-populations within your data. When you spot an outlier, the next step is to look up the raw record and understand what makes it different before deciding whether to include or exclude it in downstream analysis.
Can I add a trend line?
Trend line overlay is on the roadmap. For now, the scatter plot shows raw data points. To add a linear regression line, export the PNG and overlay it in a tool like Figma, or calculate the line equation externally and draw it manually.
How many data points can I plot?
The chart handles up to a few hundred points without performance issues. Beyond 500 points the browser canvas may slow on lower-end devices. For large datasets, consider plotting a random sample first to check the pattern before plotting the full set.
What do I do if my x and y values are on very different scales?
The axes scale independently to fit your data, so a mix of small and large numbers on different axes is handled automatically. If the difference in scale is so extreme that it compresses the interesting variation into a narrow band, consider log-transforming your values before entering them.