For AI agents: use /llms.txt for the Nakafa content index.
You surely know what frequency is, right? Frequency is simply how many times a value or data point appears in a dataset.
For example, if we ask about their phone brands, and answer "Apple", then the frequency for the "Apple" brand is .
Now, we will get acquainted with . What is it?
Relative Frequency is how we look at the frequency of a data point compared to the total number of all data points. So, it's not just about how many times it appears, but we look at the "part" or "proportion" of that data relative to the whole.
Relative frequency is very important, you know. With relative frequency, we can:
Calculating it is super easy!
Where:
Relative Frequency is usually expressed in the form of:
Let's use the phone brand data example from the earlier:
| Phone Brand | Frequency (Number of Students) |
|---|---|
| Apple | |
| Samsung | |
| Xiaomi | |
| Total |
Total frequency is students.
Now let's calculate the relative frequency for each brand:
Apple:
Relative Frequency Apple is
In percent:
Samsung:
Relative Frequency Samsung is (we round it to )
Xiaomi:
Relative Frequency Xiaomi is (we round it to )
Relative Frequency Table:
| Phone Brand | Frequency | Relative Frequency (Decimal) | Relative Frequency (Percent) |
|---|---|---|---|
| Apple | |||
| Samsung | |||
| Xiaomi | |||
| Total |
The sum of relative frequencies (in decimal form) should always be , and in percent should be . There might be slight differences due to rounding, but it should be very close to or .
From the relative frequency table above, we can say:
With relative frequency, we get a clearer picture of the proportion of each phone brand among the .
So, relative frequency helps us understand how large the "portion" of a data point is within the entire dataset. Easy, right?
In percent:
In percent:
