Suppose x has a distribution with = 10 and 2. (a) If a random sample of size n = 49 is drawn, find μ, and P(10 ≤ x ≤ 12). (Round to two decimal places and the probability to four decimal places.) H = 10 x = 0.286 X P(10 ≤ x ≤ 12) = 0.4999 (b) If a random sample of size n = 55 is drawn, find and P(10 ≤ x ≤ 12). (Round o to two decimal places and the probability to four decimal places.) H = 10 x = 0.270 P(10 ≤ x ≤ 12) = X (c) Why should you expect the probability of part (b) to be higher than that of part (a)? (Hint: Consider the standard deviations in parts (a) and (b).) The standard deviation of part (b) is smaller than ✓ part (a) because of the larger sample size. Therefore, the distribution about μ is narrower ✓
Suppose x has a distribution with = 10 and 2. (a) If a random sample of size n = 49 is drawn, find μ, and P(10 ≤ x ≤ 12). (Round to two decimal places and the probability to four decimal places.) H = 10 x = 0.286 X P(10 ≤ x ≤ 12) = 0.4999 (b) If a random sample of size n = 55 is drawn, find and P(10 ≤ x ≤ 12). (Round o to two decimal places and the probability to four decimal places.) H = 10 x = 0.270 P(10 ≤ x ≤ 12) = X (c) Why should you expect the probability of part (b) to be higher than that of part (a)? (Hint: Consider the standard deviations in parts (a) and (b).) The standard deviation of part (b) is smaller than ✓ part (a) because of the larger sample size. Therefore, the distribution about μ is narrower ✓
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
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Chapter1: Starting With Matlab
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Problem 1P
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Question
![**Title: Understanding Probability and Sample Distribution**
Suppose \( x \) has a distribution with \( \mu = 10 \) and \( \sigma = 2 \).
**Part (a):**
If a random sample of size \( n = 49 \) is drawn, find \( \mu_{\bar{x}}, \sigma_{\bar{x}} \) and \( P(10 \leq \bar{x} \leq 12) \). (Round \( \sigma_{\bar{x}} \) to two decimal places and the probability to four decimal places.)
- \( \mu_{\bar{x}} = 10 \) (✔️)
- \( \sigma_{\bar{x}} = 0.286 \) (❌)
- \( P(10 \leq \bar{x} \leq 12) = 0.4999 \) (✔️)
**Part (b):**
If a random sample of size \( n = 55 \) is drawn, find \( \mu_{\bar{x}}, \sigma_{\bar{x}} \) and \( P(10 \leq \bar{x} \leq 12) \). (Round \( \sigma_{\bar{x}} \) to two decimal places and the probability to four decimal places.)
- \( \mu_{\bar{x}} = 10 \) (✔️)
- \( \sigma_{\bar{x}} = 0.270 \) (✔️)
- \( P(10 \leq \bar{x} \leq 12) \) (❌)
**Part (c):**
Why should you expect the probability of part (b) to be higher than that of part (a)?
*Hint: Consider the standard deviations in parts (a) and (b).*
The standard deviation of part (b) is smaller than part (a) because of the larger sample size. Therefore, the distribution about \( \mu_{\bar{x}} \) is narrower. (✔️)](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F0795f56b-0a15-4f37-aa5f-bf96362a487b%2F6536c47c-f156-4dbb-9b19-01f54196559c%2Fsyh79bi_processed.png&w=3840&q=75)
Transcribed Image Text:**Title: Understanding Probability and Sample Distribution**
Suppose \( x \) has a distribution with \( \mu = 10 \) and \( \sigma = 2 \).
**Part (a):**
If a random sample of size \( n = 49 \) is drawn, find \( \mu_{\bar{x}}, \sigma_{\bar{x}} \) and \( P(10 \leq \bar{x} \leq 12) \). (Round \( \sigma_{\bar{x}} \) to two decimal places and the probability to four decimal places.)
- \( \mu_{\bar{x}} = 10 \) (✔️)
- \( \sigma_{\bar{x}} = 0.286 \) (❌)
- \( P(10 \leq \bar{x} \leq 12) = 0.4999 \) (✔️)
**Part (b):**
If a random sample of size \( n = 55 \) is drawn, find \( \mu_{\bar{x}}, \sigma_{\bar{x}} \) and \( P(10 \leq \bar{x} \leq 12) \). (Round \( \sigma_{\bar{x}} \) to two decimal places and the probability to four decimal places.)
- \( \mu_{\bar{x}} = 10 \) (✔️)
- \( \sigma_{\bar{x}} = 0.270 \) (✔️)
- \( P(10 \leq \bar{x} \leq 12) \) (❌)
**Part (c):**
Why should you expect the probability of part (b) to be higher than that of part (a)?
*Hint: Consider the standard deviations in parts (a) and (b).*
The standard deviation of part (b) is smaller than part (a) because of the larger sample size. Therefore, the distribution about \( \mu_{\bar{x}} \) is narrower. (✔️)
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