The rank correlation coefficient measures the relationship between two ranked variables. It ranges from -1 to +1, where +1 indicates a perfect positive correlation, -1 indicates a perfect negative correlation, and 0 indicates no correlation.

The formula for rank correlation coefficient is:
r = 1 – [(6 * Σd²) / (n * (n² – 1))], where d is the difference in ranks and n is the number of observations.

A positive rank correlation coefficient indicates that as one variable increases, the other variable also tends to increase.

A negative rank correlation coefficient indicates that as one variable increases, the other variable tends to decrease.

A rank correlation coefficient of 0 indicates that there is no linear relationship between the two variables.

Rank correlation is best for identifying monotonic relationships, whether linear or non-linear.

A value close to +1 indicates a strong positive correlation, while a value close to -1 indicates a strong negative correlation. A value close to 0 indicates no correlation.

Yes, rank correlation is most suitable for ordinal data where the ranks or orders of the data are meaningful.

The Spearman Rank Correlation is a non-parametric measure of correlation based on the ranks of the data rather than the raw data values.

Rank correlation (Spearman) works with ordinal data and assesses monotonic relationships, while Pearson’s correlation works with interval/ratio data and measures linear relationships.