Based on prior knowledge, it can be inferred that the relationship feature could have an impact on a person's income. The analysis will focus on identifying the relationship categories that are more likely to have a high income (earning more than 50000 dollars per year) and those that are less likely.

To conduct the analysis, we need to examine the distribution of income across different relationship categories. The goal is to identify any patterns or trends that can help differentiate between the two target classes (yes and no).

Here is the analysis of the relationship feature:

For target class "no" (earning less than or equal to 50000 dollars per year):
- Own-child: Individuals classified as "Own-child" are more likely to fall under this target class as they are dependents and typically do not have their own income.
- Not-in-family: Individuals classified as "Not-in-family" can be independent, but may not have additional financial support, which could result in lower income.

For target class "yes" (earning more than 50000 dollars per year):
- Husband: Individuals classified as "Husband" are more likely to have higher income as they may have additional financial responsibility for their family.
- Wife: Individuals classified as "Wife" could have higher income if they contribute significantly to the household's income.
- Other-relative: Individuals classified as "Other-relative" may have diverse income levels, making it difficult to predict their income.

Based on this analysis, the following dictionary can be created:

```json
{
	"no": [
		"Own-child",
		"Not-in-family"
	],
	"yes": [
		"Husband",
		"Wife",
		"Other-relative"
	]
}
```

Note that only the relationship categories that have a relatively clear distinction between the target classes are included in this dictionary.