A two-way (factorial) analysis of variance tests the effects of two categorical variables (factors) and their interaction on one continuous (response) variable.

Example: Analgesics.jmp (Help > Sample Data)

Select **Analyze > Fit Model**.

- Click on a continuous variable from
**Select Columns**, and click **Y, Response **(continuous variables have blue triangles). - Click on two categorical variables from
**Select Columns**, and click **Macros, Full Factorial **(categorical variables have red or green bars). This adds each factor and the interaction between the two factors as model effects. - Click
**OK**. The Fit Model output window will display. - Above the leverage plots select
**LS Means Plot **from the **red triangles** to display least square means plots.

Interpretation of the results in the ANOVA table under **Effects Tests**:

- The null hypothesis for a main effect is that there are no differences between the population means (i.e., all means are equal) in that factor, averaging over all other factors.
- The null hypothesis for the interaction between two effects is that the pattern of effects for one of the factors does not depend on the level of the second factor.
**Prob > F**- Both main effects are significant, indicating that the mean for males differs from the mean for females, and that not all the means for the three drugs are the same.
- We do not have evidence that the effect of drug depends on the gender of an individual, and equivalently, that the “effect” of gender does not depend on what drug someone is taking.

Tips:

- To determine which means are different (simple effects), a post hoc multiple comparison technique can be used (for details see the page
**One-Way ANOVA**). - The
**Parameter Estimates **table provides results from tests of the parameterized (dummy) variables accounting for each source of variation (factors and interactions).

Notes: For more information on two-way analysis of variance, search for **Two-Way** in the book *Fitting Linear Models* (under **Help > Books**).