Based on prior knowledge, the education level of a client can potentially have an impact on whether or not they subscribe to a term deposit. Higher education levels might indicate a higher income or a more stable financial situation, which could make clients more likely to subscribe to a term deposit. However, this is just an assumption and needs to be confirmed through data analysis.

To analyze the relationship between the feature "education" and the target variable "Does this client subscribe to a term deposit?", we can group the data by the target variable and education level and count the number of occurrences for each combination. This will allow us to see the distribution of education levels for clients who subscribe to a term deposit (yes) and those who do not (no).

Here is the dictionary with the analysis results:

```json
{
	"no": ["primary", "secondary", "unknown"],  
	"yes": ["tertiary", "primary", "secondary"]  
}
```

Explanation:
- For clients who do not subscribe to a term deposit (no), the possible values of the education level are: "primary", "secondary", and "unknown". This indicates that clients with varying education levels are not subscribing to a term deposit.
- For clients who subscribe to a term deposit (yes), the possible values of the education level are: "tertiary", "primary", and "secondary". This shows that clients with different education levels are subscribing to a term deposit.

Please note that this analysis is based on the assumption that the education level is a relevant feature for predicting whether a client subscribes to a term deposit. Actual analysis should be performed using the provided dataset to validate this assumption.