While correlation heatmaps offer a summary of linear relationships between variables, scatter plots help explore those relationships in detail. Scatter plots are especially useful for identifying outliers, clusters, and non-linear trends.

Why Use Scatter Plots?

• Visualizes the relationship between two numeric variables;
• Helps detect linear or non-linear trends;
• Allows visual spotting of outliers or anomalies;
• Supports deeper interpretation of correlation heatmap results.

Example: Selling Price vs. Max Power

ggplot(data = df, aes(x = max_power, y = selling_price)) + 
  geom_point() + 
  labs(title = "Scatter Plot of Selling Price vs. Max Power",
       x = "Max Power", 
       y = "Selling Price")

• Shows how a car's power affects its market value;
• A positive linear pattern would indicate:
  higher power → higher price.

Summary

• Scatter plots are a simple yet powerful way to study two-variable relationships;
• Use them to complement correlation matrices for deeper understanding;
• Can also help spot patterns that correlation coefficients might miss.