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

To find the value of \( E \) in the ANOVA table, we need to complete the missing information based on the relationships between the various components of the ANOVA.

The relevant formulas are:

1. \( MS = \frac{SS}{df} \) (Mean Square)
2. \( F = \frac{MS_{treatment}}{MS_{error}} \) (F-statistic)

### Given values:
- \( MS_{treatment} = 43,024.778 \)
- \( df_{treatment} = 2 \)
- \( SS_{error} = 10,254 \)

### Step-by-step calculation:

1. **Calculate \( MS_{error} \):**
Since we have the SS for the error and need the degrees of freedom (df):
\[
MS_{error} = \frac{SS_{error}}{df_{error}}
\]
We need to find \( df_{error} \) first to get \( MS_{error} \).

2. **Calculate \( df_{error} \):**
The total degrees of freedom is given by \( df_{total} = df_{treatment} + df_{error} \).
Let’s denote \( df_{error} \) as \( C \).
Thus,
\[
df_{total} = 2 + C
\]

3. **Calculate \( F \):**
The F-statistic is calculated as:
\[
F = \frac{MS_{treatment}}{MS_{error}}
\]
Rearranging this gives:
\[
MS_{error} = \frac{MS_{treatment}}{F}
\]
Plugging in the values we have:
\[
F = 25.1754
\]
\[
MS_{error} = \frac{43,024.778}{25.1754} = 1,706.847
\]

4. **Substitute \( MS_{error} \) to find \( E \):**
Now we turn to the definition of mean square to find \( E \):
\[
MS_{error} = \frac{SS_{error}}{df_{error}} \rightarrow 1,706.847 = \frac{10,254}{C}
\]
Solving for \( C \):
\[
C \cdot 1,706.847 = 10,254
\]
\[
C = \frac{10,254}{1,706.847} \approx 6
\]

5. **Calculate \( E \):**
Now that we have \( C \), we can find \( E \):
\[
E = MS_{error} = 1,706.847
\]

Thus, the value of \( E \) is approximately:

\[
\boxed{1,706.847}
\]