Decide whether the normal sampling distribution can be used. If it can be used, test the claim about the population proportion p at the given level of significance a using the given sample statistics. Claim: p 0.28; a = 0.01; Sample statistics: p= 0.21, n= 100 C. Yes, because both np and ng are greater than or equal to 5. O D. No, because ng is less than 5 State the null and alternative hypotheses. O A. Ho ps0.28 Ha p>0.28 B. Ho p=0.28 H, p#0.28 OC. Ho p20.28 H p<0.28 O D. The test cannot be performed. Determine the critical value(s). Select the correct choice below and, if necessary, fill in the answer box to complete your choice O A. The critical value(s) is/are (Round to two decimal places as needed. Use a comma to separate answers as needed.) OB. The test cannot be performed.
Decide whether the normal sampling distribution can be used. If it can be used, test the claim about the population proportion p at the given level of significance a using the given sample statistics. Claim: p 0.28; a = 0.01; Sample statistics: p= 0.21, n= 100 C. Yes, because both np and ng are greater than or equal to 5. O D. No, because ng is less than 5 State the null and alternative hypotheses. O A. Ho ps0.28 Ha p>0.28 B. Ho p=0.28 H, p#0.28 OC. Ho p20.28 H p<0.28 O D. The test cannot be performed. Determine the critical value(s). Select the correct choice below and, if necessary, fill in the answer box to complete your choice O A. The critical value(s) is/are (Round to two decimal places as needed. Use a comma to separate answers as needed.) OB. The test cannot be performed.
MATLAB: An Introduction with Applications
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ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
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![**Title: Understanding Normal Sampling Distribution in Hypothesis Testing**
**Introduction:**
This educational piece will guide you through assessing whether a normal sampling distribution can be used in hypothesis testing, using a specific example. We will also detail how to test claims about a population proportion at a given level of significance.
**Scenario Description:**
- **Claim:** \( p \neq 0.28 \)
- **Significance Level (\(\alpha\)):** 0.01
- **Sample Statistics:** \(\hat{p} = 0.21\), \( n = 100 \)
**Steps in Analysis:**
1. **Check if the Normal Distribution Can Be Used:**
- Conditions: Both \( np \) and \( nq \) must be greater than or equal to 5.
- Options:
- A: Yes, because both \( np \) and \( nq \) are greater than or equal to 5.
- B: No, because \( nq \) is less than 5.
- **Selected Answer:**
- C: Yes, because both \( np \) and \( nq \) are greater than or equal to 5.
2. **State the Null and Alternative Hypotheses:**
- Options:
- A: \[ H_0: p \geq 0.28 \quad H_a: p > 0.28 \]
- B: \[ H_0: p = 0.28 \quad H_a: p \neq 0.28 \]
- C: \[ H_0: p \leq 0.28 \quad H_a: p > 0.28 \]
- D: The test cannot be performed.
- **Selected Answer:**
- B: \[ H_0: p = 0.28 \quad H_a: p \neq 0.28 \]
3. **Determine the Critical Values:**
- Options:
- A: Calculate the critical values and round them to two decimal places.
- B: The test cannot be performed.
- **Action Required:** Fill in critical values if applicable and check answers.
**Conclusion:**
By following these steps, you can determine if the normal distribution is suitable for hypothesis testing and correctly state the hypotheses. This ensures accurate testing of claims regarding population proportions at specified significance levels.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F1c8f1796-3461-41ab-b467-ba30f7e18f8b%2F1af8d842-7bf0-4928-8ca3-61a8b7bb3f5d%2Fuvbh98_processed.jpeg&w=3840&q=75)
Transcribed Image Text:**Title: Understanding Normal Sampling Distribution in Hypothesis Testing**
**Introduction:**
This educational piece will guide you through assessing whether a normal sampling distribution can be used in hypothesis testing, using a specific example. We will also detail how to test claims about a population proportion at a given level of significance.
**Scenario Description:**
- **Claim:** \( p \neq 0.28 \)
- **Significance Level (\(\alpha\)):** 0.01
- **Sample Statistics:** \(\hat{p} = 0.21\), \( n = 100 \)
**Steps in Analysis:**
1. **Check if the Normal Distribution Can Be Used:**
- Conditions: Both \( np \) and \( nq \) must be greater than or equal to 5.
- Options:
- A: Yes, because both \( np \) and \( nq \) are greater than or equal to 5.
- B: No, because \( nq \) is less than 5.
- **Selected Answer:**
- C: Yes, because both \( np \) and \( nq \) are greater than or equal to 5.
2. **State the Null and Alternative Hypotheses:**
- Options:
- A: \[ H_0: p \geq 0.28 \quad H_a: p > 0.28 \]
- B: \[ H_0: p = 0.28 \quad H_a: p \neq 0.28 \]
- C: \[ H_0: p \leq 0.28 \quad H_a: p > 0.28 \]
- D: The test cannot be performed.
- **Selected Answer:**
- B: \[ H_0: p = 0.28 \quad H_a: p \neq 0.28 \]
3. **Determine the Critical Values:**
- Options:
- A: Calculate the critical values and round them to two decimal places.
- B: The test cannot be performed.
- **Action Required:** Fill in critical values if applicable and check answers.
**Conclusion:**
By following these steps, you can determine if the normal distribution is suitable for hypothesis testing and correctly state the hypotheses. This ensures accurate testing of claims regarding population proportions at specified significance levels.
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