Question

ANOVA summary tables typically have a “Total” row not included in the partial table you just completed. Which of the following is a possible reason for including this row?

Answer 1

The possible reason for including the "Total" row would be a. The MStotal is used in the calculation of the F test statistic.

Why put the total row in ANOVA ?

In ANOVA (Analysis of Variance), the Total row is often included in the summary tables for a specific reason. The Total row represents the overall variation in the data and is used to calculate the F test statistic, which is used to determine whether there are significant differences between the groups or treatments being compared.

The MStotal (Mean Square Total), which is obtained by dividing the SSTotal (Sum of Squares Total) by its degrees of freedom, is a critical component in the calculation of the F test statistic.

The full question is:

ANOVA summary tables typically have a "Total" row not included in the partial table you just completed. Which of the following is a possible reason for including this row?

a. The MStotal is used in the calculation of the F test statistic.

b. The SS total is sometimes easier to calculate than SSbetween. Since SS within + SS between = SStotal, you can use SStotal to calculate SS between

c. The total sums of squares is the sometimes called the "error term."

d. The SS total is used in the calculation of the F test statistic. of the between-treatments variance and the within-treatments

Answer 2

The "Total" row in an ANOVA summary table provides the overall total variation in the data, which is the sum of the between-group and within-group variation. It helps to evaluate the overall effectiveness of the model in explaining the variation in the data. Additionally, it can be used to calculate the proportion of the total variation that is accounted for by the between-group variation, which is known as the "explained variation" or the "effect size." Therefore, the inclusion of the "Total" row allows for a comprehensive understanding of the sources of variation in the data and the effectiveness of the model.