A random sample of 830 births included 425 boys. Use a 0.10 significance level to test the claim that 50.6% of newborn babies are boys. Do the results support the belief that 50.6% of newborn babies are boys?

MATLAB: An Introduction with Applications
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### Statistical Analysis of Newborn Births

In a study, a random sample of 830 births included 425 boys. The purpose is to test the claim that 50.6% of newborn babies are boys. To do this, a significance level of 0.10 is used. The question to address is: **Do the results support the belief that 50.6% of newborn babies are boys?**

#### Analytical Steps:
1. **Null Hypothesis (H0):** The proportion of boys in the population is 50.6% (p0 = 0.506).
2. **Alternative Hypothesis (H1):** The proportion of boys in the population is not 50.6% (p ≠ 0.506).
3. **Sample Proportion (p̂):** p̂ = 425/830
4. **Significance Level (α):** 0.10
5. **Z-test for Proportions:** Calculate the z-score to determine if the observed proportion significantly differs from the hypothesized population proportion.
6. **Decision Rule:** Compare the p-value to the significance level to decide whether to reject the null hypothesis.

This analysis helps determine if the observed data is consistent with the belief about the proportion of boys among newborns.
Transcribed Image Text:### Statistical Analysis of Newborn Births In a study, a random sample of 830 births included 425 boys. The purpose is to test the claim that 50.6% of newborn babies are boys. To do this, a significance level of 0.10 is used. The question to address is: **Do the results support the belief that 50.6% of newborn babies are boys?** #### Analytical Steps: 1. **Null Hypothesis (H0):** The proportion of boys in the population is 50.6% (p0 = 0.506). 2. **Alternative Hypothesis (H1):** The proportion of boys in the population is not 50.6% (p ≠ 0.506). 3. **Sample Proportion (p̂):** p̂ = 425/830 4. **Significance Level (α):** 0.10 5. **Z-test for Proportions:** Calculate the z-score to determine if the observed proportion significantly differs from the hypothesized population proportion. 6. **Decision Rule:** Compare the p-value to the significance level to decide whether to reject the null hypothesis. This analysis helps determine if the observed data is consistent with the belief about the proportion of boys among newborns.
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