The formulas you provided seem to represent the calculation of frequencies based on some observed data, possibly related to a statistical study or a probability problem. Let's break down the components:
-
Frequency (a) is calculated as: \[ \text{Frequency (a)} = \frac{(2 \times 226) + 60}{600} \] Here, 226 and 60 could represent counts or values associated with a particular event or category 'a' in your dataset. The total denominator 600 likely represents the total count of observations or the total population.
The frequency calculated here (0.85) indicates that 'a' makes up 85% of the total for whatever event or characteristic you are measuring.
-
Frequency (A) is calculated as: \[ \text{Frequency (A)} = \frac{(2 \times 14) + 60}{600} \] In this case, 14 is another count or value associated with a different event or category 'A' in your dataset. This frequency (0.15) indicates that 'A' makes up 15% of the total count.
Interpretation:
- Frequency (a) and Frequency (A) both represent proportions of different categories/events within a total of 600 units or observations.
- The formulas are used to compute relative frequencies based on observed counts.
- The '2' in both formulas suggests a scale factor or a multiplicative effect applied to the counts.
Summary:
- Frequency (a) signifies a proportion (85%) of a specific event/category in the overall dataset.
- Frequency (A) signifies a proportion (15%) of another event/category in the overall dataset.
To get a more precise meaning of a and A, context regarding the dataset or the specific problem from which these formulas arise would be necessary.