Correlation analysis is a statistical method used to evaluate the strength of a relationship between two quantitative variables. It's a widely used technique in various fields such as economics, biology, environmental science, and social sciences.

The most commonly used techniques in correlation analysis include:

1. Pearson Correlation Coefficient

• Measures: Strength and direction of linear relationships between two continuous variables.
• Assumptions: Linearity, normality of data distribution.
• Value Range: -1 (perfect negative correlation) to +1 (perfect positive correlation).

2. Spearman Rank Correlation Coefficient

• Measures: Strength and direction of monotonic relationships (consistently increasing or decreasing) between continuous or ordinal variables.
• Assumptions: Monotonicity (not necessarily linear).
• Less Sensitive to Outliers: More robust than Pearson for extreme values.
• Value Range: -1 to +1.

3. Kendall Rank Correlation Coefficient

• Measures: Strength and direction of monotonic relationships similar to Spearman.
• Computation: Different approach, often preferred for tied ranks.
• Value Range: -1 to +1

4. Point-Biserial Correlation Coefficient

• Measures: Relationship between a continuous variable and a dichotomous variable (two categories).

5. Phi Correlation Coefficient

• Measures: Relationship between two dichotomous variables.

6. Phi_K Correlation Coefficient