## Introduction

Analysis of Variance (ANOVA) is a hypothesis testing procedure that tests whether two means are significantly different from each other. One-Way ANOVA tests the relationship between a numeric variable and a categorical variable.

This article describes how to go create a One-Way ANOVA Table as shown below. The table below shows the pairwise comparison of *Total Spend* grouped by *Household description.*

## Requirements

- A numeric variable to be used as a dependent variable.
- A categorical variable to be used as a predictor.

## Method

- In the
**Anything**menu select**Advanced Analysis > Analysis of Variance > One-Way ANOVA**. - In the
**object inspector**go to the**Inputs**tab. - In the
**Output**menu select the numeric variable to be predicted by the*predictor variables.* - Select the categorical predictor variable from the
**Predictor**list. - In the
**Compare**menu select the*contrasts*to be performed.**To mean**The*post hoc*testing compares the mean of each category to the overall average (ie, the*grand mean*).**To first**The*post hoc*testing compares the mean of each category to the mean of the first category.**Pairwise**The*post hoc*testing compares the mean of each pair of categories.

- OPTIONAL: Select multiple comparison
**Correction**used when calculating*p-values*. Note: The**Correction**calculations take into account the settings in**Compare**.**Tukey Range**correction is used by default. - OPTIONAL: To compute standard errors that are robust to violations of the assumption of constant variance (ie, heteroscedasticity) select
**Robust standard errors.** - OPTIONAL: Set the
**Alternative hypothesis**to be used in computing the*p*-values in the post hoc tests. You can choose between**Two sided**(default),**Greater**or**Less.** - OPTIONAL: If the output returns an error due to missing data, go to the
**Missing Data**menu and select**Exclude Cases with Missing Data**. - OPTIONAL: Select
**Variable names**to display Variable names in the output instead of labels.

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