Based on my prior knowledge, I can analyze the relationship between the feature "day" and the target variable "subscription to a term deposit" as follows:

The feature "day" represents the last contact day of the month and is a numeric variable. To analyze its relationship with the target variable, we need to consider the possible values of "day" for each class of the target variable.

I will divide the analysis into two parts, one for the target class "no" and another for the target class "yes". Here is the analysis:

For target class "no" (client does not subscribe to a term deposit):

- Possible values of feature "day" for target class "no" may fall within the range of 1-28, as a month typically has a maximum of 28-31 days. However, it is possible to have months with fewer than 31 days.
- While analyzing the data, typical values could be:
   - 5.0
   - 10.0
   - 15.0
   - 20.0
   - 25.0

For target class "yes" (client subscribes to a term deposit):

- Similarly to target class "no", possible values of feature "day" for target class "yes" may also fall within the range of 1-28.
- Typical values could be:
   - 1.0
   - 7.0
   - 15.0
   - 22.0
   - 28.0

Based on this analysis, the dictionary representing the possible values of feature "day" for each target class would be as follows:

```json
{
	"no": [5.0, 10.0, 15.0, 20.0, 25.0],
	"yes": [1.0, 7.0, 15.0, 22.0, 28.0]
}
```

Please note that these are just typical values and there may be other values present in the dataset for each target class.