# Scatterplot Chart

A scatterplot places individual data points on a two-axis grid, where the horizontal position encodes one numeric variable and the vertical position encodes another. The resulting pattern of dots reveals whether - and how strongly - the two variables are related.

Scatterplots excel at identifying correlations and outliers between two numeric variables.

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## Demo Site Earnings

Combination chart with 2 data series.

Plotting the number of impressions vs total earnings.  
The chart has 1 X axis displaying Earnings. Data ranges from 12.54 to 2290.3.

The chart has 2 Y axes displaying Impressions and values.

Created with Highcharts 12.4.0  
Earnings Impressions  
Demo Site Earnings  
Plotting the number of impressions vs total earnings  
$0 $250 $500 $750 $1K $1.25K $1.5K $1.75K $2K $2.25K  
0600K200K400K800K1M  
Mouse over the data points to view the underlying data  
Interactive Chart  
Add a linear regression trendline to view how your data is trending  
Trend Line

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## Creating an Effective Scatterplot Chart

Recommended data types for each axis:

- **X-Axis** Numerical values (independent variable)
- **Y-Axis** Numerical values (dependent variable)

## Description

- **Points** - each dot represents one row of data; its position is determined by the X and Y values for that row
- **X-Axis** - the independent or explanatory variable
- **Y-Axis** - the dependent or outcome variable
- **Trend** - the overall direction of the point cloud (upward, downward, or flat) indicates the correlation direction

## When to Use a Scatterplot

- **Identify correlations** - see whether two numeric variables move together (positive correlation) or in opposite directions (negative correlation)
- **Detect outliers** - points far from the main cluster stand out immediately
- **Explore relationships before modeling** - scatterplots are a standard first step in statistical analysis
- **Compare two numeric datasets** - when both axes are continuous, scatterplots are the natural choice
- **Show distribution density** - dense clusters reveal where most data points concentrate

## When to Avoid a Scatterplot

- **One categorical axis** - if one variable is categorical, use a [bar chart](https://instacharts.io/docs/chart-types/bar-chart/) instead
- **Showing trends over time** - use a [line chart](https://instacharts.io/docs/chart-types/line-chart/) when the X-axis is time and the sequence matters
- **Too many overlapping points** - with very large datasets, points can overplot and obscure patterns; consider aggregating or using a [heatmap](https://instacharts.io/docs/chart-types/heatmap-chart/)
- **Multiple categories** - use a [grouped scatterplot](https://instacharts.io/docs/chart-types/grouped-scatterplot-chart/) to color-code points by a third categorical variable