# Data with two variables

### Measuring the variability

With single variable data we calculated the measure of spread to quantify the variability in the data set (e.g. standard deviation).

- The strength of the linear relationship between two variables is called the correlation coefficient (\(r\)).
- You can calculate the correlation coefficient on most scientific calculators.
- The correlation coefficient is a value that can only be between \(−1\) and \(1\).
- The closer the correlation coefficient is to either \(-1\) or \(1\), the stronger the linear correlation will be.
- A negative correlation coefficient means that the relationship is negative (as \(x\) increases, \(y\) decreases). A positive correlation coefficient means that the relationship is positive (as \(x\) increases, \(y\) increases).
- For example the correlation coefficient is \(r \approx 0.966\).
- This implies a strong positive relationship between the two variables. To note: the presence of a strong linear correlation does not imply a cause/effect relationship.