Scatter Plots and Correlations

Scatter plots are essential for exploring relationships between two continuous variables. They can reveal correlations, clusters, and outliers that would be invisible in summary statistics.

Basic Scatter Plot

Visualizing Correlation

The pattern in a scatter plot reveals the correlation:

Adding Trend Lines

A trend line (regression line) shows the relationship direction:

Color-Coded Categories

Use color to show a third categorical variable:

Size as a Third Variable (Bubble Charts)

Use point size to represent a third continuous variable:

Color Mapping with Colorbar

Map a continuous variable to color:

Highlighting Outliers

Identify and highlight outliers in your data:

Quadrant Analysis

Divide your scatter plot into meaningful quadrants:

Practice: Correlation Analysis

Key Takeaways

• Scatter plots reveal relationships between two continuous variables
• The pattern shows correlation: positive (up-right), negative (up-left), or none (random)
• Add trend lines with np.polyfit() and np.poly1d()
• Use color to show categorical groups or a third continuous variable
• Use size (bubble charts) to add another dimension
• Identify outliers using z-scores or visual inspection
• Quadrant analysis helps categorize data points

In the next lesson, we'll learn to style line and scatter plots for maximum visual impact.