Tutorial Exercise Consider a binomial random variable with n=9 and p= 0.7. Let x be the number of successes in the sample. Evaluate the probability. P(3 ≤ x ≤ 6) Step 1 A binomial experiment consists of n identical trials with probability of success p on each trial. The table of cumulative binomial probabilities gives probability values of P(x s k) for the random variable x with k successes. The graph of the binomial distribution for n = 9 and p= 0.7 is below. X 0.30 0.25 0.20 0.15 0.10 0.05 0.00L 0 1 2 3 4 5 6 7 8 9 X Ⓡ , we can Of interest is P(3 ≤ x s 6), which is represented by the bars x = 3, 4, 5, 6. The table will give the cumulative probability P(x s 6) by summing the areas for the bars x = 0, 1, 2, 3, 4, 5, and 6. Since we do not need the area for x = 0, 1,2 ✔ subtract the area for these bars from the area for all seven bars. Thus, the needed probability can be found using P(x ≤ 6)- P(xs

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**Tutorial Exercise**

Consider a binomial random variable with \( n = 9 \) and \( p = 0.7 \). Let \( x \) be the number of successes in the sample. Evaluate the probability.

\[ P(3 \leq x \leq 6) \]

**Step 1**

A binomial experiment consists of \( n \) identical trials with probability of success \( p \) on each trial. The table of cumulative binomial probabilities gives probability values of \( P(x \leq k) \) for the random variable \( x \) with \( k \) successes. The graph of the binomial distribution for \( n = 9 \) and \( p = 0.7 \) is below.

**Graph:**

- The graph is a histogram illustrating the binomial distribution of the probabilities of \( x \), the number of successes. 
- The x-axis represents the number of successes ranging from 0 to 9.
- The y-axis represents the probability \( P(x) \) of observing exactly \( x \) successes.
- The bars indicate the probability mass function of the binomial distribution for each \( x \).

**Explanation:**

Of interest is \( P(3 \leq x \leq 6) \), which is represented by the bars \( x = 3, 4, 5, 6 \). The table will give the cumulative probability \( P(x \leq 6) \) by summing the areas for the bars \( x = 0, 1, 2, 3, 4, 5, \) and \( 6 \). Since we do not need the area for \( x = 0, 1, 2 \), we can subtract the area for these bars from the area for all seven bars. Thus, the needed probability can be found using \( P(x \leq 6) - P(x \leq 2) \).
Transcribed Image Text:**Tutorial Exercise** Consider a binomial random variable with \( n = 9 \) and \( p = 0.7 \). Let \( x \) be the number of successes in the sample. Evaluate the probability. \[ P(3 \leq x \leq 6) \] **Step 1** A binomial experiment consists of \( n \) identical trials with probability of success \( p \) on each trial. The table of cumulative binomial probabilities gives probability values of \( P(x \leq k) \) for the random variable \( x \) with \( k \) successes. The graph of the binomial distribution for \( n = 9 \) and \( p = 0.7 \) is below. **Graph:** - The graph is a histogram illustrating the binomial distribution of the probabilities of \( x \), the number of successes. - The x-axis represents the number of successes ranging from 0 to 9. - The y-axis represents the probability \( P(x) \) of observing exactly \( x \) successes. - The bars indicate the probability mass function of the binomial distribution for each \( x \). **Explanation:** Of interest is \( P(3 \leq x \leq 6) \), which is represented by the bars \( x = 3, 4, 5, 6 \). The table will give the cumulative probability \( P(x \leq 6) \) by summing the areas for the bars \( x = 0, 1, 2, 3, 4, 5, \) and \( 6 \). Since we do not need the area for \( x = 0, 1, 2 \), we can subtract the area for these bars from the area for all seven bars. Thus, the needed probability can be found using \( P(x \leq 6) - P(x \leq 2) \).
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