Scatter Chart

Free Online Scatter Chart Maker

What is a Scatter Chart?

A scatter chart plots individual data points across two axes to reveal relationships between two variables. Each dot represents one observation — its horizontal position shows one value, its vertical position shows another. Use scatter charts when you want to see whether two variables move together, diverge, or show no pattern at all. They are especially effective at surfacing clusters, trends, and outliers that tables and summaries would hide.

Key Features

Correlation Detection

Plot two variables against each other to immediately see positive, negative, or no correlation — without running a formula.

Outlier Identification

Individual data points are always visible, so anomalies and extreme values stand out at a glance rather than getting averaged away.

Variable Point Sizing

Encode a third dimension by adjusting dot size per point, turning a basic scatter plot into a bubble-style view when needed.

Reference Lines

Add horizontal or vertical reference lines to mark thresholds, targets, or averages and give each data point meaningful context.

Labeled Data Points

Optionally display a name or value next to each dot so viewers can identify specific observations without hovering.

Custom Axis Labels

Name both axes clearly so your audience always knows what each dimension represents, even outside the chart.

Best For

Correlation analysis between two numeric variables

Outlier and anomaly detection in datasets

Scientific or research data with paired measurements

Sales performance vs. marketing spend comparisons

Student test scores vs. study hours analysis

Quality control: defect rate vs. production volume

When to Use

You have two numeric variables and want to see if they are related
Your dataset has individual observations that matter, not just aggregates
You suspect outliers exist and want to make them visible
You need to compare actual vs. predicted values point by point
You want to show distribution density across two dimensions
A line chart would be misleading because your X axis is not time-ordered

Common Mistakes

Connecting dots with lines when the X axis is not a continuous sequence
Omitting axis labels so viewers cannot tell what the variables actually are
Plotting categorical data on one axis, which distorts the apparent relationship
Overloading the chart with hundreds of overlapping points without transparency or jitter
Assuming a visual cluster proves causation rather than correlation
Using inconsistent dot sizes when size is not encoding a meaningful third variable

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Templates

Start with professionally designed templates

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