A scatter chart (also called a scatter plot) is a data visualization tool used to display the relationship between two variables by plotting data points on a two-dimensional graph. Each point on the chart represents a single observation in the dataset, with its position determined by the values of two variables—one mapped to the X-axis and the other to the Y-axis.
Scatter charts are widely used to identify trends, correlations, clusters, and outliers in data, making them essential for exploratory data analysis.
Key Components of a Scatter Chart:
Data Points:
Each point on the chart represents an individual observation.
The X and Y coordinates of each point correspond to the values of the two variables being analyzed.
X-Axis:
Represents the independent variable or input variable.
Example: Time, temperature, or any variable influencing the outcome.
Y-Axis:
Represents the dependent variable or output variable.
Example: Sales, revenue, or any variable affected by the input.
Trend Line (Optional):
A line fitted to the data points to indicate the overall direction or pattern, such as linear or exponential relationships.
Clusters and Patterns:
Groupings of data points that may signify subcategories, relationships, or shared characteristics.
Outliers:
Points that deviate significantly from the general pattern, potentially indicating errors, rare events, or special cases.
When to Use a Scatter Chart?
Scatter charts are particularly useful in the following scenarios:
Analyzing Relationships:
Ideal for identifying correlations (positive, negative, or none) between two variables. For example, examining the relationship between marketing spend and sales revenue.
Identifying Trends:
Scatter charts can highlight linear, exponential, or other trends in data.
Detecting Clusters:
Useful for identifying groupings or patterns, which may suggest different categories or segments within the data.
Spotting Outliers:
Outliers become immediately apparent on a scatter chart, allowing for further investigation.
Comparing Variables:
Effective for comparing how two variables interact across multiple data points.