To develop a probability model based on the observed frequencies, we need to calculate the probability for each letter. The probabilities can be calculated by dividing the frequency of each letter by the total number of trials (in this case, 100).
The observed frequencies are:
- C: 20
- A: 25
- R: 30
- E: 10
- S: 15
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Calculate Total Frequency: \[ \text{Total} = 20 + 25 + 30 + 10 + 15 = 100 \]
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Calculate Probabilities:
- \( P(C) = \frac{20}{100} = 0.20 \)
- \( P(A) = \frac{25}{100} = 0.25 \)
- \( P(R) = \frac{30}{100} = 0.30 \)
- \( P(E) = \frac{10}{100} = 0.10 \)
- \( P(S) = \frac{15}{100} = 0.15 \)
The resulting probability model is:
\[ \begin{array}{|c|c|c|c|c|c|} \hline X & C & A & R & E & S \ \hline P(X) & 0.20 & 0.25 & 0.30 & 0.10 & 0.15 \ \hline \end{array} \]
Thus, the correct response from the options provided is: \[ X \quad C \quad A \quad R \quad E \quad S \ P(X) \quad 0.20 \quad 0.25 \quad 0.30 \quad 0.10 \quad 0.15 \]