To ensure that your results are valid, consider the following guidelines when you collect data, perform the analysis, and interpret your results.
If your design contains covariates, use Fit General Linear Model.
The categorical factors can be crossed and nested factors, and fixed and random factors.
For more information on factors, go to Factors and factor levels, What are factors, crossed factors, and nested factors?, and What is the difference between fixed and random factors?.
The requirement for balanced data extends to nested factors as well. Suppose A has 3 levels, and B is nested within A. If B has 4 levels within the first level of A, B must have 4 levels within the second and third levels of A. Minitab will tell you if you have unbalanced nesting. The requirement that data be balanced must be preserved after missing data are omitted.
If your design is not balanced, use Fit General Linear Model.
For more information about balanced designs, go to Balanced and unbalanced designs.
Random samples are used to make generalizations, or inferences, about a population. If your data were not collected randomly, your results might not represent the population.
If the model does not fit the data, the results can be misleading. In the output, use residual plots and model summary statistics to determine how well the model fits the data.