Based on prior knowledge, we can analyze the relationship between the feature "day" (last contact day of the month) and the task of whether the client subscribes to a term deposit.

To conduct the analysis, we can examine the distribution of the feature values for each target class and identify typical day values for clients who did not subscribe (target class: 'no') and clients who did subscribe (target class: 'yes').

Here is the analysis and the corresponding dictionary:

```json
{
	"no": [5.0, 15.0, 25.0, 28.0, 31.0],
	"yes": [1.0, 10.0, 20.0, 30.0, 31.0]
}
```

Explanation:
- For the target class 'no', the feature values in the list [5.0, 15.0, 25.0, 28.0, 31.0] represent typical last contact days of the month.
- For the target class 'yes', the feature values in the list [1.0, 10.0, 20.0, 30.0, 31.0] represent typical last contact days of the month.

Please note that the choice of these specific values is subjective and can vary depending on the dataset and the analysis performed.