A marginal plot is a visualization technique that combines a scatter plot with histograms (or density plots) along the x-axis and y-axis margins. This allows for a deeper understanding of the relationships between two continuous variables while also displaying their individual distributions.

Structure of a Marginal Plot

A marginal plot consists of:

1. Central Scatter Plot – Represents the relationship between two continuous variables.
2. Marginal Distributions – Histograms or density plots along the axes that show the distribution of each variable individually.
3. Additional Enhancements  – Some marginal plots include box plots or violin plots in the margins for more detailed distribution insights.

Why Is a Marginal Plot Used?

• The scatter plot in the center helps identify correlations, clusters, and trends between two numerical variables.
• Example: Analyzing the relationship between sales revenue and advertising budget in a business dataset.

• The histograms or density plots on the margins provide a quick view of how each variable is distributed.
• Example: Checking if the advertising budget follows a normal distribution or is skewed.

• Helps in identifying clusters of data points and unusual outliers that may not be apparent in a scatter plot alone.
• Example: A company might notice that most sales occur within a specific budget range but with a few extreme outliers.

• The shape of the histograms or density plots can reveal skewness, kurtosis, or multimodal distributions.
• Example: In finance, checking whether stock returns follow a normal distribution.

• Combining multiple plots in a single visualization makes it easier to interpret data without needing separate histograms or density plots.