The quadrant count ratio (QCR) is a measure of the association between two quantitative variables. The QCR is not commonly used in the practice of statistics; rather, it is a useful tool in statistics education because it can be used as an intermediate step in the development of Pearson's correlation coefficient.^[1]

Definition and properties

To calculate the QCR, the data are divided into quadrants based on the mean of the ${\displaystyle X}$ and ${\displaystyle Y}$ variables. The formula for calculating the QCR is then:

${\displaystyle q={\frac {n({\text{Quadrant I)))+n({\text{Quadrant III)))-n({\text{Quadrant II)))-n({\text{Quadrant IV)))}{N)),}$

where ${\displaystyle {\text{n(Quadrant)))}$ is the number of observations in that quadrant and ${\displaystyle N}$ is the total number of observations.^[2]

The QCR is always between −1 and 1. Values near −1, 0, and 1 indicate strong negative association, no association, and strong positive association (as in Pearson's correlation coefficient). However, unlike Pearson's correlation coefficient the QCR may be −1 or 1 without the data exhibiting a perfect linear relationship.

Example

The scatterplot shows the maximum wind speed (X) and minimum pressure (Y) for 35 Category 5 Hurricanes. The mean wind speed is 170 mph (indicated by the blue line), and the mean pressure is 921.31 hPa (indicated by the green line). There are 6 observations in Quadrant I, 13 observations in Quadrant II, 5 observations in Quadrant III, and 11 observations in Quadrant IV. Thus, the QCR for these data is ${\displaystyle {\frac {(6+5)-(13+11)}{35))=-0.37}$, indicating a moderate negative relationship between wind speed and pressure for these hurricanes. The value of Pearson's correlation coefficient for these data is −0.63, also indicating a moderate negative relationship..