To analyze the relationship between the feature "Monetary" and the target variable "Did the person donate blood?", we need to consider the typical ranges of values for the feature and how they are related to the target variable.

In this case, the feature "Monetary" represents the total amount of blood donated in cubic centimeters (c.c.) by a person. We can assume that higher values of "Monetary" indicate that a person has donated more blood. 

Based on prior knowledge, here is a analysis of the relationship between the feature and target:

1. Higher Monetary values are more likely to be associated with the target variable "yes", indicating that the person has donated blood.
2. Lower Monetary values are more likely to be associated with the target variable "no", indicating that the person has not donated blood.

To create the dictionary with specific details, we can assume typical Monetary values for each target class:

```json
{
	"yes": [300, 500, 800, 1000, 1500],
	"no": [0, 50, 100, 200, 250]
}
```

Please note that these values are just assumptions and may not accurately represent the actual data. You should use a larger dataset to determine the appropriate ranges of values for the feature.