Based on the given task, we want to determine the relationship between the feature "Monetary" (total blood donated in c.c.) and the target variable "Did the person donate blood?". 

To analyze this relationship, we can consider the following:

1. If a person has donated blood (target class "yes"), it is likely that they have donated a significant amount of blood. Thus, we can expect higher values of the "Monetary" feature for this class.

2. On the other hand, if a person has not donated blood (target class "no"), it is unlikely that they have donated any measurable amount of blood. Therefore, we can expect lower or zero values of the "Monetary" feature for this class.

Based on this analysis, we can create a dictionary:

```json
{
    "yes": [400.0, 600.0, 800.0, 1000.0, 1200.0],
    "no": [0.0, 0.0, 0.0, 0.0, 0.0]
}
```

In this dictionary, we include 5 typical Monetary values for each target class. For the "yes" class, the values are [400.0, 600.0, 800.0, 1000.0, 1200.0], indicating higher amounts of blood donated. For the "no" class, the values are [0.0, 0.0, 0.0, 0.0, 0.0], indicating no blood donation.