A researcher, studying stress is interested in the blood pressure measurements of chief executive officers CEOs of major corporations yes, good reason to believe that the main systolic blood pressure of CEOs of major corporations is different from 130 to MMHG which is the value reported in a possibly outdated journal article, he plans to perform a statistical test. He measures the systolic blood pressure of a random sample of CEOs of major corporations, and find the mean of the sample to be 126 MMHG and the standard deviation of the sample to be 15 MMHG. 

What are the nI’ll hypothesis H0 and 

In this scenario, the researcher is interested in testing whether the mean systolic blood pressure of CEOs of major corporations differs from 130 mmHg, which is the value reported in an outdated journal article.

### Null Hypothesis (\(H_0\)):
The null hypothesis represents the assumption that there is no difference between the population mean systolic blood pressure of CEOs and the value reported in the article (130 mmHg). Formally, it is stated as:
\[ H_0: \mu = 130 \text{ mmHg} \]
Where \(\mu\) is the true mean systolic blood pressure of CEOs.

### Alternative Hypothesis (\(H_a\)):
The alternative hypothesis represents the researcher's belief that the mean systolic blood pressure of CEOs is different from 130 mmHg. This could be a two-tailed test, where the researcher is interested in any difference, whether higher or lower than 130 mmHg. Formally, it is stated as:
\[ H_a: \mu \neq 130 \text{ mmHg} \]

In summary:
- **Null Hypothesis (\(H_0\))**: The mean systolic blood pressure of CEOs is 130 mmHg (\(\mu = 130\)).
- **Alternative Hypothesis (\(H_a\))**: The mean systolic blood pressure of CEOs is not 130 mmHg (\(\mu \neq 130\)).

Would you like to go through the steps to perform the statistical test, or do you have any other questions?

### Related Questions:
1. How do you determine the type of statistical test needed for this hypothesis?
2. What is the significance level, and how does it affect hypothesis testing?
3. How do you calculate the test statistic for this hypothesis test?
4. What is a p-value, and how is it interpreted in hypothesis testing?
5. How do you calculate confidence intervals for the mean systolic blood pressure?
6. What are Type I and Type II errors in the context of this hypothesis test?
7. How would the sample size affect the results of the hypothesis test?
8. How do you interpret the results if the p-value is less than the significance level?

### Tip:
When conducting hypothesis tests, always ensure you clearly define your hypotheses, significance level, and understand the consequences of Type I and Type II errors to draw valid conclusions from your data.

Understand hypothesis testing for the mean systolic blood pressure of CEOs of major corporations. Learn how to perform a Z-test with a sample mean of 126 mmHg and a standard deviation of 15 mmHg against a null hypothesis of 130 mmHg. Gain insights into statistical significance and interpretation of results.

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