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

1. Divide the range of monetary values into meaningful intervals (ranges). For example, if the values range from 0 to 1000 c.c., we can divide it into intervals such as 0-200 c.c., 201-400 c.c., 401-600 c.c., 601-800 c.c., and 801-1000 c.c.

2. Analyze the proportion of people who donated blood and did not donate blood within each interval (range). This can be done by looking at historical data or by conducting a survey.

3. Identify typical Monetary values that are associated with both classes ('yes' and 'no') based on the analysis. These values should represent the range of values where the proportion of people who donated blood and did not donate blood is relatively higher.

Based on this analysis, let's create the dictionary:

```json
{
	"yes": [200.0, 400.0, 600.0, 800.0, 1000.0],
	"no": [0.0, 100.0, 300.0, 500.0, 700.0]
}
```

Please note that the values provided in the dictionary are hypothetical and depend on the actual data and analysis you perform on the relationship between the feature and the target task.