Based on prior knowledge, the occupation can be a strong indicator of a person's income. Certain occupations tend to have higher earning potential than others. 

To analyze the relationship between the occupation feature and the task of whether a person earns more than 50000 dollars per year, we can examine the distribution of occupations in the dataset for each target class (yes and no).

Here is the dictionary for the occupation feature and its relationship with the target variable:

```json
{
	"no": ["Machine-op-inspct", "Farming-fishing", "Other-service", "Priv-house-serv", "Handlers-cleaners"],  
	"yes": ["Protective-serv", "Prof-specialty", "Exec-managerial", "Tech-support", "Sales", "Craft-repair", "Adm-clerical", "Transport-moving"]  
}
```

In the "no" category, the occupations are more likely to be of lower-paying types such as "Machine-op-inspect," "Farming-fishing," "Other-service," "Priv-house-serv," and "Handlers-cleaners."

In the "yes" category, the occupations are more likely to be of higher-paying types such as "Protective-serv," "Prof-specialty," "Exec-managerial," "Tech-support," "Sales," "Craft-repair," "Adm-clerical," and "Transport-moving."

Keep in mind that this analysis is based on general assumptions about occupations and may not hold true for every individual in the dataset.