To summarize the data, we first need to compute the following values for each class of the dataset:

1. Frequency: The number of times values within each class appear in the dataset.
2. Relative Frequency: The frequency of each class divided by the total number of data points, rounded to three decimal places.
3. Percent Frequency: The relative frequency multiplied by 100, giving the result as a percentage rounded to one decimal place.

Step 1: Organize the data into classes

It seems that this dataset is a list of numbers, but before moving forward, we need to define the classes (or intervals) you want to group the data into. Classes can be ranges like 10-14, 15-19, 20-24, etc. Here's one way we could define the classes for your dataset:

Class	Frequency	Relative Frequency	Percent Frequency
10-14
15-19
20-24
25-29

Step 2: Compute the frequencies

The next step is to count how many values fall into each class range.

Would you like me to calculate the frequencies and complete the table with this approach? If you had other specific classes in mind, let me know!