Types of Correlation – Positive, Negative & Zero

Correlation tells us how two variables move in relation to each other. But the nature of that movement differs — sometimes they rise together, sometimes move in opposite directions, and sometimes show no relation at all.

In this chapter, we explore the three fundamental types of correlation with simple diagrams and real-life business examples.

Meaning of Types of Correlation

Correlation may be:

• Positive → both variables move in the same direction
• Negative → variables move in opposite directions
• Zero → variables show no systematic relationship

These relationships help businesses, economists, and researchers understand patterns and make decisions.

Positive Correlation

Positive correlation occurs when one variable increases, the other also increases. Likewise, when one decreases, the other decreases.

Examples:

• Income ↑ and Expenditure ↑
• Advertising ↑ and Sales ↑
• Rainfall ↑ and Crop Yield ↑

Diagram (Positive)

  /
 /
/

As X increases, Y also increases.

Interpretation

• Values tend to move together.
• Correlation coefficient r → +1 indicates perfect positive correlation.

Negative Correlation

Negative correlation occurs when one variable increases, the other decreases.

Examples:

• Price ↑ and Demand ↓
• Speed ↑ and Travel Time ↓
• Machine Age ↑ and Productivity ↓

Diagram (Negative)

\
 \
  \

As X increases, Y decreases.

Interpretation

• Variables move in opposite directions.
• r → –1 indicates perfect negative correlation.

Zero Correlation

Zero correlation means no relationship between variables. Changes in one variable do NOT predict changes in the other.

Examples:

• Shoe size vs Intelligence
• Height vs Income
• Mobile data usage vs Number of pets

Diagram (Zero)

.  .    .  .   .   .
 .   .  .   .  .

No pattern: points are scattered randomly.

Interpretation

• r ≈ 0
• No linear relationship exists between X and Y.

Additional Classifications

Correlation can also be classified based on degree:

1. Perfect Correlation (r = ±1)

All points lie exactly on a straight line.

2. High Correlation (|r| close to 1)

Strong relationship.

3. Low Correlation (|r| close to 0)

Weak relationship.

4. Linear vs Non-linear Correlation

• Linear: points follow a straight-line pattern
• Non-linear: rate of change differs (e.g., learning curve)

Business Applications

• Marketing: Ad spends vs sales
• Finance: Risk vs return
• HR: Experience vs performance
• Economics: Income vs consumption
• Health sector: Exercise vs BP levels

Correlation type helps businesses understand how strongly decisions influence outcomes.

Important Notes

• Correlation only measures relationship, not cause-effect.
• Positive and negative correlations can vary from weak to strong.
• Zero correlation does not mean variables are unrelated — they may still have a non-linear relationship.

Summary

• Positive correlation: move together
• Negative correlation: move opposite
• Zero correlation: no relationship
• Strength varies from perfect → high → low → zero
• Linear and non-linear patterns exist
• Widely used in business, economics, finance, and research

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