Visualizing Correlations with Scatter Plots
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.
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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.
¡Gracias por tus comentarios!
