Based on prior knowledge, we can analyze the relationship between the feature "checking_status" and the target variable "Does this person receive a credit?".

The "checking_status" feature represents the status of the existing checking account of an individual. It is a categorical variable with four possible values: '<0', '0<=X<200', 'no checking', and '>=200'. 

To analyze the relationship with the target variable, we can look at the distribution of the checking_status values for those who receive a credit (yes) and those who do not receive a credit (no).

Possible values of checking_status for target class 'yes':
- 'no checking': People without a checking account may be more likely to receive credit as they may not have any outstanding debt or financial obligations.

Possible values of checking_status for target class 'no':
- '<0', '0<=X<200', '>=200': People with a checking account and their balance falls under these categories may have higher chances of being denied credit as it indicates some financial instability or potential risk.

Based on this analysis, we can create the following dictionary:
```json
{
	"yes": ["no checking"],
	"no": ["<0", "0<=X<200", ">=200"]
}
```

In this case, we only include the values that provide clear insights into the relationship between the feature and the target variable.