Unusual observations (also called influential observations) are observations that have a disproportionate impact on a regression or ANOVA model. Unusual observations are important to identify because they can produce misleading results. For example, an unusual observation can cause a significant coefficient to seem insignificant.
The observations that Minitab labels do not follow the proposed regression equation well. However, it is expected that you will have some unusual observations. For example, based on the criteria for large residuals, you would expect roughly 5% of your observations to be flagged as having a large residual.
In the previous output, observation 1 is denoted with an X, identifying it as a leverage point. Observation 22, denoted with an R, is an outlier.
To determine how much effect the unusual observation has, you can fit the model with and without the observation and compare the coefficients, p-values, R2, and other model parameters. If the model changes significantly when you remove the unusual observation, first, determine whether the observation is a data entry or measurement error. If not, examine the model more to determine whether you omitted an important term (for example, an interaction term) or variable, or have incorrectly specified the model. You might need to collect more data to determine a resolution.