Based on prior knowledge and understanding the task at hand, we can analyze the relationship between the feature "Monetary" (total blood donated in c.c.) and the target variable "Did the person donate blood?".

To generate the requested dictionary, we need to identify typical Monetary values for each target class ('yes' and 'no').

Since the task is to determine if a person donated blood or not, we can assume that individuals who donated blood would have nonzero values for Monetary. On the other hand, individuals who did not donate blood would have Monetary values of zero.

With this in mind, we can create the following dictionary:

```json
{
	"yes": [100.0, 200.0, 300.0, 400.0, 500.0],
	"no": [0.0, 0.0, 0.0, 0.0, 0.0]
}
```

In the above example, we included 5 values for each target class. For the 'yes' class, we assumed typical Monetary values of 100.0, 200.0, 300.0, 400.0, and 500.0 (represented as floats). For the 'no' class, we assumed all Monetary values to be 0.0.

Please note that the specific values may vary depending on the dataset and the nature of the relationship.