Based on prior knowledge, we can analyze the relationship between the 'Monetary' feature and the target variable of whether a person donated blood or not.

Since the 'Monetary' feature represents the total blood donated in c.c., it is reasonable to assume that individuals who have donated more blood (higher 'Monetary' values) are more likely to have donated blood in the past. 

To create the dictionary with the requested format, we can identify typical 'Monetary' values for both the 'yes' and 'no' classes. Here is an example of such a dictionary:

```json
{
	"yes": [250.0, 500.0, 750.0, 1000.0, 1500.0],
	"no": [0.0, 30.0, 80.0, 120.0, 200.0]
}
```

In this example, we assume that individuals with 'Monetary' values such as 250.0, 500.0, 750.0, 1000.0, and 1500.0 are more likely to have donated blood ('yes' class), while individuals with 'Monetary' values 0.0, 30.0, 80.0, 120.0, and 200.0 are more likely to not have donated blood ('no' class).

Please note that these typical values are purely hypothetical and may vary depending on the actual dataset and underlying relationships.