To analyze the relationship between the education feature and the target variable (earning more than 50000 dollars per year), we can examine the distribution of education levels for each target class.

Based on prior knowledge, we can anticipate that individuals with higher levels of education, such as Masters or Doctorate, are more likely to earn over 50000 dollars per year. On the other hand, individuals with lower education levels, such as Preschool or 1st-4th grade, are less likely to earn over 50000 dollars.

However, it is important to analyze the data to confirm these assumptions.

Let's create the dictionary with the specified format:

```json
{
	"no": ["Preschool", "1st-4th", "5th-6th", "7th-8th", "9th", "10th", "11th", "12th", "HS-grad", "Some-college"],

	"yes": ["Assoc-acdm", "Assoc-voc", "Bachelors", "Prof-school", "Masters", "Doctorate"]
}
```

In this dictionary, the "no" key represents the target class where an individual does not earn more than 50000 dollars per year. The associated list contains the possible values of the education feature for this target class.

The "yes" key represents the target class where an individual does earn more than 50000 dollars per year. The associated list contains the possible values of the education feature for this target class.

Please note that the above lists are based on general assumptions and may differ depending on the specific dataset being analyzed.