Consider the following partially completed ANOVA, and answer the following: give the value of (B). / Beskou die onvoltooide ANOVA en beantwoord die volgende: gee die waarde van (B).

To determine the value of (B) in the ANOVA table, we can use the relationship between the total sum of squares (SS), the treatment SS, and the error SS.

The total sum of squares (SS) is the sum of the treatment SS and the error SS:

\[
B = \text{Treatment SS} + \text{Error SS}
\]

From the provided information:
- Treatment SS = 12,689
- Error SS = A (we don't have the value of A yet)

The degrees of freedom (df) for the total is given as 11.

The degrees of freedom total (df_total) is calculated as the sum of the degrees of freedom for treatment and error:

\[
df_{total} = df_{treatment} + df_{error}
\]

From the ANOVA table structure:
- Let \( C \) be the degrees of freedom for treatment.
- Let \( E \) be the degrees of freedom for error.

The degrees of freedom for treatment is often the number of groups minus one. Since we do not have the exact number of treatment groups, we cannot directly calculate (C). However, we can express total degrees of freedom as:

\[
df_{total} = C + E = 11
\]

To calculate (B), we need the value of (A). Additionally, we know the mean squares (MS) for treatment and error can also relate their sum of squares.

The mean square (MS) for treatment is defined as:

\[
MS_{treatment} = \frac{SS_{treatment}}{df_{treatment}} = \frac{12689}{C}
\]

The mean square (MS) for error is defined as:

\[
MS_{error} = \frac{SS_{error}}{df_{error}} = \frac{A}{E}
\]

We're provided that:

\[
F_{stat} = \frac{MS_{treatment}}{MS_{error}} = 10.6642
\]

Therefore:

\[
MS_{error} = \frac{12689}{C \cdot 10.6642}
\]

If we reorganize the composition of total sums of squares, we can isolate (B) after we get the values for A or see if anything can confirm both (A) and (C) from the available data.

However, since you are looking specifically for (B), given \( B = 12,689 + A \):

1. If we assume perhaps a few degrees of freedom typically for simple cases, a common way might be a 1-way ANOVA with 3 groups (2 df).

So that means:
- Treatment df (C) = 2
- Error df (E) = 9 (from df_total 11 - df_treatment 2)

Thus now we can calculate:

So

A (Error SS) = \( MS_{error} = 595.3611 \cdot E \)
= \( 595.3611 \cdot 9 \)
= 5,358.250
Round off approx to 5,358

Then substituting back into the total:

\[
B = 12,689 + 5,358 \approx 18,047
\]

Finally, with values afforded and calculations along the steps, therefore \( B \approx 18,047\) as total sum of squares in the ANOVA table.

### Conclusion:
**The value of (B) is approximately 18,047.**