The factor information table displays the factors in the design along with information about the type of factors, the number of levels, and the values of the levels.

## Interpretation

Use the factor information table to verify that you performed the analysis as you intended. Factors are the variables that you control in the experiment. Factors are also known as independent variables, explanatory variables, and predictor variables. Factors can assume only a limited number of possible values, known as factor levels. Factors can take text or numeric values. Numeric factors use a few controlled values in the experiment, even though many values are possible.

In balanced ANOVA, factors can be either fixed or random. In general, if the investigator controls the levels of a factor, the factor is fixed. On the other hand, if the investigator randomly sampled the levels of a factor from a population, the factor is random.

For example, a quality analyst plans to study factors that could affect plastic strength during the manufacturing process. The analyst includes Additive, Temperature, and Operator in the experiment. The additive is a categorical variable which can be type A or type B. Temperature is a continuous variable but the analyst plans to include only three temperatures settings in the experiment: 100 °C, 150 °C, and 200 °C. Because the analyst controls the levels of these two factors in the experiment, these factors are both fixed. On the other hand, the analyst decides to randomly select operators from the plant population. Consequently, Operator is a random factor.

### General Linear Model: C4 versus Additive, Temperature, Operator

Factor Information
Factor Type Levels Values
Additive Fixed 2 A, B
Temperature Fixed 3 100, 150, 200
Operator Random 3 Amy, Fred, Morgan