Background: I am studying a course on statistical experiments using the textbook by Douglas Montgomery on the analysis of experiments. This is an introductory course and so I am relatively new to the study of these types of models.
Question: Consider the model $Y_{i,j,k} = \mu + R_i + C_j + T_k + \epsilon_{i,j,k}$ where $R_i$ is the impact of from row $i$, $C_j$ is the impact from column $j$ and $T_k$ is the impact from treatment $k$ where there are exactly $n$ rows, $n$ columns, and $n$ treatments. We know that the row sum of squares $S^2_{\text{row}} = \sum _ {i=1}^n \frac{Y_{i \cdot \cdot}^2}{n} - \frac{Y_{\cdot \cdot \cdot}^2}{n^2}$ and similarly for $S^2_{\text{column}}$ and $S^2_{\text{treatment}}$ we have the same form but instead we sum over $j$ and $k$ respectively from $1$ to $n$ (we take the formulae for the sums of squares directly from the corresponding ANOVA table).
I have read in the textbook (without justification) the formula for the expected value $ \color{red}{\mathbb{E}(S^2_{\text{row}}\space)}$ and (again) the same applies for the columns and treatments by replacing $R_i^2$ with $C_j^2$ (for the columns) and $T_k^2$ (for the treatments).
$\text{How do we find the formula for the expectation?}$
Note: we are making the standard modelling assumptions for these types of models where the error terms are independent and normally distributed with mean $0$ and variance $\sigma^2$.