Based on prior knowledge, the education level is likely to be a strong predictor of income. Generally, individuals with higher levels of education tend to earn more than those with lower levels of education. Therefore, we can expect to see a positive relationship between education and income.

To create the required dictionary, we need to analyze the relationship between education and income and identify the possible values of education for each target class.

Here is the analysis:

- Possible values of education for target class 'no' (earning less than 50000 dollars per year):
['11th', 'HS-grad', '9th', '7th-8th', '10th', '1st-4th', '12th', '5th-6th']

- Possible values of education for target class 'yes' (earning more than 50000 dollars per year):
['Assoc-acdm', 'Some-college', 'Prof-school', 'Bachelors', 'Masters', 'Doctorate', 'Assoc-voc', 'Preschool']

Based on this analysis, we can now create the dictionary:

```json
{
	"no": ["11th", "HS-grad", "9th", "7th-8th", "10th", "1st-4th", "12th", "5th-6th"],
	"yes": ["Assoc-acdm", "Some-college", "Prof-school", "Bachelors", "Masters", "Doctorate", "Assoc-voc", "Preschool"]
}
```

This dictionary represents the possible values of education for each target class. For individuals earning less than 50000 dollars per year (target class 'no'), we have identified 8 possible education values. For individuals earning more than 50000 dollars per year (target class 'yes'), we have identified 8 other education values.

Feel free to reach out if you have any further questions!