Assume that a simple random sample has been selected from a normally distributed population and test the given claim. Identify the null and alternative hypotheses, test statistic, P-value, and state the final conclusion that addresses the original claim. A safety administration conducted crash tests of child booster seats for cars. Listed below are results from those tests, with the measurements given in hic (standard head injury condition units). The safety requirement is that the hic measurement should be less than 1000 hic. Use a 0.01 significance level to test the claim that the sample is from a population with a mean less than 1000 hic. Do the results suggest that all of the child booster seats meet the specified requirement? 823 781 1301 657 661 523 D What are the hypotheses? O A. Ho: = 1000 hic O B. Ho: u< 1000 hic H,: µ2 1000 hic H,:µ2 1000 hic OC. Ho: H> 1000 hic H,:µ< 1000 hic O D. Ho: u = 1000 hic H,: µ< 1000 hic

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**Hypothesis Testing for Child Booster Seat Safety**

_Assume that a simple random sample has been selected from a normally distributed population and test the given claim. Identify the null and alternative hypotheses, test statistic, P-value, and state the final conclusion that addresses the original claim._

A safety administration conducted crash tests of child booster seats for cars. Listed below are results from those tests, with the measurements given in hic (standard head injury condition units). The safety requirement is that the hic measurement should be less than 1000 hic. Use a 0.01 significance level to test the claim that the sample is from a population with a mean less than 1000 hic. Do the results suggest that all of the child booster seats meet the specified requirement?

Measurements (hic): 823, 781, 1301, 657, 661, 523 

**What are the hypotheses?**

**A.** \( H_0: \mu = 1000 \text{ hic} \)
   \\
   \( H_1: \mu \ne 1000 \text{ hic} \)

**B.** \( H_0: \mu < 1000 \text{ hic} \)
   \\
   \( H_1: \mu \ge 1000 \text{ hic} \)

**C.** \( H_0: \mu > 1000 \text{ hic} \)
   \\
   \( H_1: \mu < 1000 \text{ hic} \)

**D.** \( H_0: \mu = 1000 \text{ hic} \)
   \\
   \( H_1: \mu < 1000 \text{ hic} \)

In this testing scenario, the null hypothesis (\( H_0 \)) and the alternative hypothesis (\( H_1 \)) need to be clearly defined to verify if the sample data supports the safety requirement of having hic measurements less than 1000 hic with a significance level (\( \alpha \)) of 0.01. The correct identification of hypotheses is critical to determining the validity of the safety claim.

Consider these options carefully and evaluate the potential of each hypothesis set to validate the requirement based on the test results.
Transcribed Image Text:**Hypothesis Testing for Child Booster Seat Safety** _Assume that a simple random sample has been selected from a normally distributed population and test the given claim. Identify the null and alternative hypotheses, test statistic, P-value, and state the final conclusion that addresses the original claim._ A safety administration conducted crash tests of child booster seats for cars. Listed below are results from those tests, with the measurements given in hic (standard head injury condition units). The safety requirement is that the hic measurement should be less than 1000 hic. Use a 0.01 significance level to test the claim that the sample is from a population with a mean less than 1000 hic. Do the results suggest that all of the child booster seats meet the specified requirement? Measurements (hic): 823, 781, 1301, 657, 661, 523 **What are the hypotheses?** **A.** \( H_0: \mu = 1000 \text{ hic} \) \\ \( H_1: \mu \ne 1000 \text{ hic} \) **B.** \( H_0: \mu < 1000 \text{ hic} \) \\ \( H_1: \mu \ge 1000 \text{ hic} \) **C.** \( H_0: \mu > 1000 \text{ hic} \) \\ \( H_1: \mu < 1000 \text{ hic} \) **D.** \( H_0: \mu = 1000 \text{ hic} \) \\ \( H_1: \mu < 1000 \text{ hic} \) In this testing scenario, the null hypothesis (\( H_0 \)) and the alternative hypothesis (\( H_1 \)) need to be clearly defined to verify if the sample data supports the safety requirement of having hic measurements less than 1000 hic with a significance level (\( \alpha \)) of 0.01. The correct identification of hypotheses is critical to determining the validity of the safety claim. Consider these options carefully and evaluate the potential of each hypothesis set to validate the requirement based on the test results.
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