N-Way ANOVA Task

About the N-Way ANOVA Task

Example: Analyzing the Sashelp.RevHub2 Data Set

Assigning Data to Roles

Building a Model

Requirements for Building a Model

Create a Main Effect

Create Crossed Effects (Interactions)

Create a Nested Effect

Create a Full Factorial Model

Create an N-Way Factorial

Setting Options

Setting the Output Options

About the N-Way ANOVA Task

The N-Way ANOVA task tests and provides graphs for effects of one or more factors on the means of a single, continuous dependent variable.

Example: Analyzing the Sashelp.RevHub2 Data Set

To create this example:

In the Tasks section, expand the Statistics folder and double-click N-Way ANOVA. The user interface for the N-Way ANOVA task opens.
On the Data tab, select the SASHELP.REVHUB2 data set.

Assign variables to these roles:

Role	Column Name
Dependent variable	Revenue
Factors	Source Type

On the Model tab, select Source and Type. Click Full Factorial.
To run the task, click .

Here is a subset of the results:

Class Level Information for the N-Way ANOVA Example

Assigning Data to Roles

To run the N-Way ANOVA task, you must assign columns to the Dependent variable and Factors roles.

Role	Description
Dependent variable	specifies the dependent variable.
Factors	specifies the classification variables.

Building a Model

Requirements for Building a Model

By default, no effects are specified, which results in the task fitting an intercept-only model. To run the task, you must assign at least two variables to the Factors role. You can select combinations of variables to create crossed, nested, factorial, or polynomial effects.

To create a model, use the model builder on the Model tab. After you create your model, you can specify whether to include the intercept in the model.

Create a Main Effect

Select the variable name in the Variables box.
Click Add to add the variable to the Model effects box.

Create Crossed Effects (Interactions)

Select two or more variables in the Variables box. To select more than one variable, press Ctrl.
Click Cross.

Create a Nested Effect

Nested effects are specified by following a main effect or crossed effect with a classification variable or list of classification variables enclosed in parentheses. The main effect or crossed effect is nested within the effects listed in parentheses. Here are examples of nested effects: B(A), C(B*A), D*E(C*B*A). In this example, B(A) is read "A nested within B."

Select the effect name in the Model effects box.
Click Nest. The Nested window opens.
Select the variable to use in the nested effect. Click Outer or Nested within Outer to specify how to create the nested effect.

Note: The Nested within Outer button is available only when a classification variable is selected.
Click Add.

Create a Full Factorial Model

Select two or more variables in the Variables box.
Click Full Factorial.

For example, if you select the Height, Weight, and Age variables and then click Full Factorial, these model effects are created: Age, Height, Weight, Age*Height, Age*Weight, Height*Weight, and Age*Height*Weight.

Create an N-Way Factorial

Select two or more variables in the Variables box.
Click N-way Factorial to add these effects to the Model effects box.

For example, if you select the Height, Weight, and Age variables and then specify the value of N as 2, when you click N-way Factorial, these model effects are created: Age, Height, Weight, Age*Height, Age*Weight, and Height*Weight. If N is set to a value greater than the number of variables in the model, N is effectively set to the number of variables.

Setting Options

Option	Description
Statistics
You can choose to display only the default statistics, the default statistics and additional statistics, or no statistics in the output. Here are the options for the additional statistics: Perform multiple comparisons computes the least squares means for the specified effects. You can specify the method for adjustments for the p-values and confidence limits for the differences of the least squares means. The Sum of Squares options enable you to display the sum of squares associated with Type I estimable functions for each effect and the sum of squares associated with Type III estimable functions for each effect.
Plots
You can choose to display only the default plots, only selected plots, or no plots in your output. You can specify the maximum number of points to display in the plots. Here are some plots that you can include in your results: least squares means plot mean difference plot interaction plot (available only if there are two variables assigned to the Factors role) analysis of means plot (available only if you select the Nelson method for adjustment) diagnostic plots, which can be displayed individually or in a panel

Setting the Output Options

You can specify whether to create an output data set. You can also specify the values to include in the output data set. You can include predicted values, residuals, standard errors, and influence statistics.