Output: 1.0
This indicates a perfect positive linear relationship for this small example.
🔹 18. Common Mistakes
• Thinking correlation must be between 0 and 1 → Correlation can be negative: -1 <= r <= 1
• Thinking r = 0 means absolutely no relationship → It means there is no linear relationship detected by Pearson correlation. A nonlinear relationship may still exist.
• Assuming high correlation proves causation → Correlation only tells us that variables move together. It does not establish cause and effect.
🎯 Key Takeaways
• Covariance measures how two variables change together.
• Positive covariance indicates that variables tend to move in the same direction.
• Negative covariance indicates that they tend to move in opposite directions.
• Correlation measures the direction and strength of a linear relationship.
• Pearson correlation ranges from -1 to +1.
• Correlation is unitless and easier to interpret than covariance.
• A correlation of +1 indicates perfect positive linear association.
• A correlation of -1 indicates perfect negative linear association.
• A correlation of 0 indicates no linear association.
• Correlation does not imply causation.
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8August 25, 2026 1.6K 9
