Based on prior knowledge and understanding of the feature "day" in the context of a banking term deposit subscription, it is likely that the day of the month may have some influence on the decision to subscribe.

Here is the analysis and the corresponding dictionary:

```json
{
	"no": [1.0, 2.0, 3.0, 28.0, 29.0],
	"yes": [15.0, 20.0, 25.0, 30.0, 31.0]
}
```

Explanation:
- For the target class "no" (not subscribing to a term deposit), possible typical values of the feature "day" could be 1.0, 2.0, 3.0, 28.0, and 29.0. These values represent the beginning and end of the month, which might indicate that clients are less likely to subscribe at these times.
- For the target class "yes" (subscribing to a term deposit), possible typical values of the feature "day" could be 15.0, 20.0, 25.0, 30.0, and 31.0. These values represent mid and end-of-month dates, which might indicate a higher likelihood of subscription as clients might have more disposable income towards the end of the month.

It is important to note that these are just examples of typical values and it is necessary to verify the relationship between the feature and target using appropriate statistical tests or machine learning algorithms.