Based on prior knowledge, the marital status of a client may have an impact on whether they subscribe to a term deposit. Let's analyze the relationship between the marital status and the target variable.

To do this analysis, we can look at the distribution of marital statuses for clients who subscribed to a term deposit and those who did not.

Let's assume we have a dataset with the following information:

| Client ID | Marital Status | Term Deposit |
|-----------|----------------|--------------|
| 1         | married        | no           |
| 2         | single         | yes          |
| 3         | divorced       | no           |
| 4         | single         | no           |
| 5         | married        | yes          |

Analyzing this dataset, we can see that:

- For clients who did not subscribe to a term deposit (target class: 'no'), the possible marital statuses are: ['married', 'divorced', 'single'].
- For clients who subscribed to a term deposit (target class: 'yes'), the possible marital status is: ['single'].

Based on this analysis, we can create the dictionary:

```json
{
	"no": ["married", "divorced", "single"],
	"yes": ["single"]
}
```

Please note that the values in the resulting dictionary are case-sensitive.