**Analysis:**

In order to analyze the relationship between the "own_telephone" feature and the target variable "Does this person receive a credit?", we can look at the likelihood of each category of "own_telephone" for each target class.

From prior knowledge, it is expected that having a telephone may be considered as a positive factor in determining creditworthiness. However, it is also possible that having no telephone could be an indication of financial instability or lack of credit history.

We can infer the relationship by examining the distribution of values for the "own_telephone" feature among the "yes" and "no" target classes.

**Dictionary:**

Based on the analysis, the dictionary presenting the relationship between the "own_telephone" feature and the "Does this person receive a credit?" target variable is as follows:

```json
{
	"yes": ["yes"],  
	"no": ["none"]
}
```
In this case, the only possible value for the "own_telephone" feature that is associated with the "yes" target class is "yes". Conversely, the only possible value associated with the "no" target class is "none".

Note that only the relevant values for each target class are included in the dictionary.