Question

(b) a relative frequency distribution.
average precipitation (inches) relative frequency
0–4.99
5–9.99
10–14.99
15–19.99
20–24.99
25–29.99
30–34.99
35–39.99
40–44.99
45–49.99
50–54.99
55–59.99
60–64.99

Explanation:

To solve this, we need the frequency (number of observations) in each class interval. Since the frequencies are missing, we'll assume we have a dataset with total number of observations \( N \). The relative frequency for each class is calculated as \( \text{Relative Frequency} = \frac{\text{Frequency of the class}}{N} \).

Step 1: Obtain Frequencies

First, we need the frequency (count) of data points in each interval (e.g., 0–4.99, 5–9.99, etc.). Let’s denote the frequency of a class as \( f_i \) and the total number of observations as \( N \).

Step 2: Calculate Relative Frequency

For each class interval, compute:
\[
\text{Relative Frequency}_i = \frac{f_i}{N}
\]

Example (If Frequencies Were Given)

Suppose we have a dataset with \( N = 100 \) and frequencies:

• 0–4.99: \( f_1 = 10 \)
• 5–9.99: \( f_2 = 15 \)
• 10–14.99: \( f_3 = 20 \)

-... (and so on for other classes)

Then:

• Relative Frequency for 0–4.99: \( \frac{10}{100} = 0.10 \)
• Relative Frequency for 5–9.99: \( \frac{15}{100} = 0.15 \)

-...

Final Answer

To complete the relative frequency distribution, follow these steps:

1. Count the frequency (\( f_i \)) of each class interval.
2. Divide each \( f_i \) by the total number of observations (\( N \)) to get the relative frequency.

If you provide the frequencies or the dataset, we can compute the exact relative frequencies. For now, the formula to use is \( \text{Relative Frequency} = \frac{\text{Frequency of the class}}{\text{Total number of observations}} \).

Answer:

To solve this, we need the frequency (number of observations) in each class interval. Since the frequencies are missing, we'll assume we have a dataset with total number of observations \( N \). The relative frequency for each class is calculated as \( \text{Relative Frequency} = \frac{\text{Frequency of the class}}{N} \).

Step 1: Obtain Frequencies

First, we need the frequency (count) of data points in each interval (e.g., 0–4.99, 5–9.99, etc.). Let’s denote the frequency of a class as \( f_i \) and the total number of observations as \( N \).

Step 2: Calculate Relative Frequency

For each class interval, compute:
\[
\text{Relative Frequency}_i = \frac{f_i}{N}
\]

Example (If Frequencies Were Given)

Suppose we have a dataset with \( N = 100 \) and frequencies:

• 0–4.99: \( f_1 = 10 \)
• 5–9.99: \( f_2 = 15 \)
• 10–14.99: \( f_3 = 20 \)

-... (and so on for other classes)

Then:

• Relative Frequency for 0–4.99: \( \frac{10}{100} = 0.10 \)
• Relative Frequency for 5–9.99: \( \frac{15}{100} = 0.15 \)

-...

Final Answer

To complete the relative frequency distribution, follow these steps:

1. Count the frequency (\( f_i \)) of each class interval.
2. Divide each \( f_i \) by the total number of observations (\( N \)) to get the relative frequency.

If you provide the frequencies or the dataset, we can compute the exact relative frequencies. For now, the formula to use is \( \text{Relative Frequency} = \frac{\text{Frequency of the class}}{\text{Total number of observations}} \).