Select the analysis options for Correlation

Stat > Basic Statistics > Correlation > Options
Select the Pearson correlation or the Spearman correlation.
Pearson correlation
Use the Pearson correlation coefficient to examine the strength and direction of the linear relationship between two continuous variables.The Pearson correlation is the most common method for correlation.

For example, an engineer can use the Pearson correlation coefficient to determine whether increases in a facility's temperature are associated with decreases in the thickness of chocolate coating.

Spearman correlation

Use the Spearman correlation coefficient (also known as Spearman's rho) when the relationship between variables is not linear. The Spearman correlation measures the monotonic relationship between two continuous or ordinal variables. In a monotonic relationship, the variables tend to move in the same relative direction, but not necessarily at a constant rate. In a linear relationship, the variables move in the same direction at a constant rate. For more information, go to Linear, nonlinear, and monotonic relationships.

The Spearman correlation coefficient is often used to evaluate relationships with ordinal variables. If your data are continuous, Minitab ranks the raw data before performing the correlation.

For example, a manager ranks employees in the order they complete a test exercise. The manager can use the Spearman correlation coefficient to evaluate whether the employee's rank is related to the number of months they have been employed.

For more information, go to A comparison of the Pearson and Spearman correlation methods.

Confidence level
In Confidence level, enter the level of confidence for the confidence interval.
Usually, a confidence level of 95% works well. A 95% confidence level indicates that, if you take 100 random samples from the population, the confidence intervals for approximately 95 of the samples would contain the correlation coefficient.
For a given set of data, a lower confidence level produces a narrower confidence interval, and a higher confidence level produces a wider confidence interval. The width of the interval also tends to decrease with larger sample sizes. Therefore, you may want to use a confidence level other than 95%, depending on your sample size.
  • If your sample size is small, a 95% confidence interval may be too wide to be useful. Using a lower confidence level, such as 90%, produces a narrower interval. However, the likelihood that the interval contains the correlation coefficient decreases.
  • If your sample size is large, consider using a higher confidence level, such as 99%. With a large sample, a 99% confidence level may still produce a reasonably narrow interval, while also increasing the likelihood that the interval contains the correlation coefficient.
Store correlation matrix
Store the correlation matrix in the worksheet. Minitab stores each matrix with the name CORR1, CORR2, and so on. If you want to display the matrix after you've stored it, choose Data > Display Data.