Hi in a mixed effects model can be used to identify data points with high leverage settings for fixed effect terms only. The design matrix used to calculate Hi is the design matrix for fixed effect terms.
Hi values fall between 0 and 1. Minitab identifies observations with leverage values greater than 3p/n or 0.99, whichever is smaller, with an X in the Marginal Fits and Diagnostics for Unusual Observations table. In 3p/n, p is the number of coefficients in the model, and n is the number of observations. The observations that Minitab labels with an 'X' may be influential.
Influential observations have a disproportionate effect on the model and can produce misleading results. For example, the inclusion or exclusion of an influential point can change whether a coefficient is statistically significant or not. Influential observations can be leverage points, outliers, or both.
If you see an influential observation, determine whether the observation is a data-entry or measurement error. If the observation is neither a data-entry error nor a measurement error, determine how influential an observation is. First, fit the model with and without the observation. Then, compare the coefficients, p-values, R2, and other model information. If the model changes significantly when you remove the influential observation, examine the model further to determine if you have incorrectly specified the model. You may need to gather more data to resolve the issue.