Based on the feature description and task, we can analyze the relationship between the "day" feature and whether the client subscribes to a term deposit.

Typically, the "day" feature represents the last contact day of the month. It is a numeric variable, and based on prior knowledge, we can determine the appropriate ranges of values. 

To analyze the relationship, we can plot the distribution of the "day" feature for both the "no" and "yes" classes. This will help us identify any patterns or differences in the distribution of values.

After analyzing the data, we can create a dictionary in the given format:

```json
{
	"no": [25.0, 12.0, 28.0, 4.0, 20.0],
	"yes": [15.0, 19.0, 6.0, 3.0, 21.0]
}
```

In this dictionary, the "no" key represents the target class "no", and the associated values represent typical "day" values for the "no" class. Similarly, the "yes" key represents the target class "yes", and the associated values represent typical "day" values for the "yes" class.

These values are just examples and represent some typical "day" values for each target class. The actual values may vary depending on the data and the analysis conducted.