The major attribute of correlation analysis is the association among variables. It is a statistical technique used to measure and quantify the strength and direction of the relationship between two variables, indicating how one variable changes in relation to another
. Key points about this attribute include:
- Correlation analysis identifies the degree of association —whether positive, negative, or none—between variables
- It quantifies this association using a correlation coefficient (commonly Pearson’s r), which ranges from -1 to +1, where values close to ±1 indicate a strong linear relationship and values near 0 indicate little or no linear relationship
- The analysis helps reveal patterns and dependencies in data but does not imply causation ; a correlation indicates association, not that one variable causes changes in the other
- It is primarily based on the assumption of a linear relationship between quantitative variables
Thus, the fundamental and major attribute of correlation analysis is to detect and measure the association between variables to understand how they relate to each other statistically
