Based on prior knowledge, we can assume that age may have some correlation with income. Generally, as individuals gain more experience and reach their peak earning potential, their income tends to increase. However, it is important to note that age alone may not be the sole determinant of income. Other factors such as education, occupation, and location should also be considered.

To perform the analysis, we can examine the age distribution for individuals earning more than 50000 dollars per year and for those earning less than or equal to 50000 dollars per year, segmented into 'yes' and 'no' target classes.

Based on this analysis, the dictionary format for the relationship between age and the target variable can be as follows:

```json
{
  "no": [18, 23, 32, 47, 52],
  "yes": [34, 39, 43, 55, 60]
}
```

Note that this is a sample dictionary and the actual values may vary depending on the dataset used for analysis. Additionally, I have provided 5 typical age values for each target class, but it's important to analyze the entire age distribution to draw more accurate conclusions.