Based on prior knowledge, the feature "Monetary" represents the total amount of blood donated in c.c. (cubic centimeters). To analyze the relationship between this feature and the task of whether a person donated blood or not, we can consider the following:

1. People who have donated blood in the past might have a higher total blood donated in c.c. compared to those who have not donated.

2. There may be a certain threshold value of "Monetary" that separates individuals who donate blood from those who don't. For example, individuals with a higher "Monetary" value might be more likely to donate blood.

Based on these considerations, we can analyze the relationship between the feature "Monetary" and the task of whether a person donated blood or not. To do this, we need a dataset that includes information on the "Monetary" feature and the corresponding target variable indicating whether a person donated blood or not.

Assuming we have such a dataset, we can create a dictionary with the specified format. Here is an example:

```json
{
	"yes": [350.0, 500.0, 600.0, 800.0, 1000.0],
	"no": [50.0, 100.0, 200.0, 300.0, 400.0]
}
```

In this example, we have included 5 typical Monetary values for each target class. The "yes" class represents individuals who donated blood, while the "no" class represents individuals who did not donate blood. The values in the "yes" and "no" lists are float values that represent different amounts of blood donated in c.c. Note that these values are just examples and may vary depending on the actual dataset used for analysis.