4) The following table summarizes the results from a two-factor study with 3 levels of factor A and three levels of factor B using a separate sample of n = 8 participants in each treatment condition. Fill in the missing values. (Hint: Start with the df values.)

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**Analysis of Variance (ANOVA) Table for a Two-Factor Study**

In this example, we examine a two-factor study with 3 levels of Factor A and 3 levels of Factor B. Each treatment condition has a sample size of \( n = 8 \) participants. This table outlines the sum of squares (SS), degrees of freedom (df), and mean squares (MS) for different sources of variance.

| Source              | SS  | df  | MS | F    |
|---------------------|-----|-----|----|------|
| Between treatments  | 108 | ___ | ___|      |
| **Factor A**        | ___ | ___ | ___|      |
| **Factor B**        | ___ | ___ | ___| F = 6.0 |
| **A × B Interaction** | ___ | ___ | 12 |      |
| Within treatments   | 189 | ___ | ___|      |
| **Total**             | ___ | ___ |   |      |

### Explanation of Table Components:

- **Source**: This column lists the sources of variance tested in the ANOVA. It includes Between treatments, Factor A, Factor B, A × B Interaction, Within treatments, and Total.

- **SS (Sum of Squares)**: This represents the total variability attributed to each source. The values need to be determined.

- **df (Degrees of Freedom)**: This column shows the degrees of freedom associated with each source. The values need to be calculated starting with this column, as suggested in the hint.

- **MS (Mean Squares)**: This is calculated by dividing the sum of squares by the respective degrees of freedom. The values need to be filled in.

- **F (F-value)**: This statistic is used in hypothesis testing to determine if the variance between sample means is significant. The F-value for Factor B is provided as 6.0, while other values need to be calculated. 

The goal is to fill in the missing values starting with the degrees of freedom (df) based on the information provided and established formulas for ANOVA calculations.
Transcribed Image Text:**Analysis of Variance (ANOVA) Table for a Two-Factor Study** In this example, we examine a two-factor study with 3 levels of Factor A and 3 levels of Factor B. Each treatment condition has a sample size of \( n = 8 \) participants. This table outlines the sum of squares (SS), degrees of freedom (df), and mean squares (MS) for different sources of variance. | Source | SS | df | MS | F | |---------------------|-----|-----|----|------| | Between treatments | 108 | ___ | ___| | | **Factor A** | ___ | ___ | ___| | | **Factor B** | ___ | ___ | ___| F = 6.0 | | **A × B Interaction** | ___ | ___ | 12 | | | Within treatments | 189 | ___ | ___| | | **Total** | ___ | ___ | | | ### Explanation of Table Components: - **Source**: This column lists the sources of variance tested in the ANOVA. It includes Between treatments, Factor A, Factor B, A × B Interaction, Within treatments, and Total. - **SS (Sum of Squares)**: This represents the total variability attributed to each source. The values need to be determined. - **df (Degrees of Freedom)**: This column shows the degrees of freedom associated with each source. The values need to be calculated starting with this column, as suggested in the hint. - **MS (Mean Squares)**: This is calculated by dividing the sum of squares by the respective degrees of freedom. The values need to be filled in. - **F (F-value)**: This statistic is used in hypothesis testing to determine if the variance between sample means is significant. The F-value for Factor B is provided as 6.0, while other values need to be calculated. The goal is to fill in the missing values starting with the degrees of freedom (df) based on the information provided and established formulas for ANOVA calculations.
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