To compute the total value for $(Error)^2$ in this data set, you simply sum up all the given values in the $(Error)^2$ column. Here is the computation step-by-step:

Given data:

\text{(Error)^2 values: } 0.0025, 0.0361, 0.0195, 0.0196, 0.0001, 0.0001, 0.0361, 0.0027, 0.0011

Adding these values:

$0.0025 + 0.0361 + 0.0195 + 0.0196 + 0.0001 + 0.0001 + 0.0361 + 0.0027 + 0.0011 = 0.1178$

Total $(Error)^2$: $\mathbf{0.1178}$

Let me know if you need further explanation or want this in a different format!

Here are some questions to expand on this:

1. Would you like a visualization of these errors, such as a bar chart?
2. Do you want to calculate the Mean Squared Error (MSE) or another metric?
3. Should I analyze trends or deviations between the forecast and actual values?
4. Do you need assistance interpreting these results in a real-world context?
5. Would you like help automating calculations like these for larger datasets?

Tip: Always verify that your forecast values align with actual trends to minimize error over time.