Scatter Plot

Purpose

Visualize the relationship between two continuous variables to identify patterns, correlations, trends, and outliers.

Analysis Type

Bivariate

What to Look For

1. Linearity

• Non-Linear: Points form a curve (quadratic, exponential, logarithmic), U/S shape waves or saturation/plateau.
• Linear: Points form a roughly straight line pattern (positive or negative) with no obvious bending.
• No relationship: Points scattered randomly

2. Correlation Strength

• Strong: Points tightly clustered around a pattern
• Weak: Points loosely scattered
• None: Random cloud of points

3. Direction

• Positive: As X increases, Y increases
• Negative: As X increases, Y decreases

4. Outliers

• Points far from the main cluster
• May indicate data quality issues or interesting cases

5. Heteroscedasticity

• Variance of Y changes across X values
• Fan-shaped pattern indicates non-constant variance

Code Example

# Basic scatter plot
plt.scatter(df['x_var'], df['y_var'], alpha=0.6)
plt.title("Scatter Plot: X vs Y")
plt.xlabel("X Variable")
plt.ylabel("Y Variable")
plt.show()

# Seaborn scatter with regression line
sns.regplot(x='x_var', y='y_var', data=df, lowess=True, line_kws={"color": "red"})
plt.title("Scatter Plot with Linear Fit")
plt.show()