Let's classify the results into consistent or inconsistent based on the given model. The model expectations for each category (assumed as per our context for this problem) are:
- Model Pasta orders per day: 130
- Model Salad orders per day: 30
- Model Enchiladas orders per day: 30
We are also determining consistency using a threshold of ±20.
Here's the step-by-step classification:
### Day 1 Results:
- Pasta: 130 (consistent, as it matches the model exactly)
- Salad: 37 (consistent, as 30 ± 20 includes 37)
- Enchiladas: 38 (consistent, as 30 ± 20 includes 38)
### Day 2 Results:
- Pasta: 175 (inconsistent, as it exceeds the range 130 ± 20)
- Salad: 17 (consistent, as 30 ± 20 includes 17)
- Enchiladas: 14 (consistent, as 30 ± 20 includes 14)
### Day 3 Results:
- Pasta: 105 (inconsistent, as it is below the range 130 ± 20)
- Salad: 31 (consistent, as 30 ± 20 includes 31)
- Enchiladas: 33 (consistent, as 30 ± 20 includes 33)
### Total of All 3 Days Results:
- Total Pasta: [tex]\(130 + 175 + 105 = 410\)[/tex] (consistent with the model expectation: 130 3 = 390 ± 60)
- Total Salad: [tex]\(37 + 17 + 31 = 85\)[/tex] (consistent with the model expectation: 30 3 = 90 ± 60)
- Total Enchiladas: [tex]\(38 + 14 + 33 = 85\)[/tex] (consistent with the model expectation: 30 * 3 = 90 ± 60)
### Classification Table
\begin{tabular}{|c|c|}
\hline \begin{tabular}{c}
Consistent with \\
Model
\end{tabular} & \begin{tabular}{c}
Inconsistent with \\
Model
\end{tabular} \\
\hline
Day 1 results & Day 2 Pasta results \\
Day 2 Salad results & Day 3 Pasta results \\
Day 2 Enchiladas results & \\
Day 3 Salad results & \\
Day 3 Enchiladas results & \\
Total of all 3 days results & \\
\hline
\end{tabular}
The results are summarized as consistent or inconsistent based on our model expectations and given thresholds.
