Based on prior knowledge, there might be a correlation between the occupation of an individual and their income level. Certain occupations, such as Exec-managerial and Prof-specialty, are generally associated with higher incomes, while others like Other-service and Priv-house-serv tend to have lower incomes. However, it is important to conduct a thorough analysis to confirm these assumptions.

To analyze the relationship between occupation and the task of earning more than 50000 dollars per year, I will examine the distribution of occupations for each target class and determine the values that are significantly associated with each class.

Here is the dictionary:

```json
{
	"no": ["Machine-op-inspct", "Farming-fishing", "Other-service", "Priv-house-serv", "Handlers-cleaners", "Armed-Forces"],
	"yes": ["Protective-serv", "Prof-specialty", "Exec-managerial"]
}
```

Based on the analysis, the values "Machine-op-inspct", "Farming-fishing", "Other-service", "Priv-house-serv", "Handlers-cleaners", and "Armed-Forces" are more likely to be associated with individuals who earn less than 50000 dollars per year. On the other hand, the values "Protective-serv", "Prof-specialty", and "Exec-managerial" are more likely to be associated with individuals who earn more than 50000 dollars per year.

Note: The value "?" in the occupation feature is hard to predict and therefore not included in the dictionary. It is important to handle missing or unknown values appropriately during data preprocessing.