Based on prior knowledge, education level can be a strong predictor of income. Higher levels of education are often associated with higher income levels. Therefore, it is likely that individuals with higher education levels, such as a Master's or Doctorate degree, are more likely to earn over $50,000 per year.

To analyze the relationship between the feature "education" and the target variable "Does this person earn more than $50,000 per year?", we can examine the distribution of education levels for each target class.

Based on this analysis, the dictionary would look like:

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

This means that individuals with education levels of "11th", "HS-grad", "9th", "7th-8th", "5th-6th", "10th", "1st-4th", "12th", or "Preschool" are more likely to earn less than $50,000 per year. On the other hand, individuals with education levels of "Assoc-acdm", "Some-college", "Prof-school", "Bachelors", "Masters", or "Doctorate" are more likely to earn over $50,000 per year.

Please note that this analysis is based on general assumptions and may not hold true in every specific case. It is always important to validate the relationship between features and target variables using appropriate statistical techniques.