Based on prior knowledge, skin thickness can be an indicator of diabetes. Higher values of skin thickness may suggest a higher likelihood of having diabetes. To analyze the relationship between the feature "SkinThickness" and the target variable "Does this person have diabetes?", we can consider the following approach:

1. Identify common skin thickness values for individuals with diabetes (target class "yes") and without diabetes (target class "no").
2. Determine the appropriate ranges of skin thickness values for each target class.
3. Create a dictionary with the required format, including five typical skin thickness values for each target class.

Based on the above analysis, the dictionary can be created as follows:

```json
{
	"yes": [33.0, 40.0, 44.0, 50.0, 54.0],
	"no": [20.0, 25.0, 30.0, 35.0, 38.0]
}
```

Note: The specific values provided above are for illustrative purposes only and may not reflect the actual relationship between skin thickness and diabetes. Further analysis and domain expertise are required to derive accurate and meaningful conclusions.