Based on prior knowledge, the job type can potentially impact whether a client subscribes to a term deposit. Certain job types may have a higher likelihood of subscribing to a term deposit, while others may have a lower likelihood.

To analyze the relationship between the target variable (whether the client subscribes to a term deposit) and the feature (job type), we can examine the distribution of job types for both the "yes" and "no" classes of the target variable.

After conducting the analysis, we can create the dictionary as follows:

```json
{
	"no": ["management", "technician", "blue-collar", "unknown", "retired", "admin.", "services", "self-employed", "unemployed", "housemaid", "student"],
	"yes": ["management", "technician", "entrepreneur", "retired", "admin.", "self-employed", "housemaid"]
}
```

Please note that this analysis is based on prior knowledge and assumptions. The specific relationship between the job type and target variable would need to be validated using appropriate statistical analysis or machine learning algorithms.