f) Test the claim that µ = 15. Provide a sketch of the Critical Value. Evaluate the Test Statistic and provide the p- value.

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**Data Set Analysis**

Use the following data set: **40, 33, 77, 12, 23, 56, 23, 19, 29** (in minutes). Assume the data is approximately bell-shaped and is a sample.

In this dataset, each number represents a time duration recorded in minutes. The assumption that the data is "approximately bell-shaped" suggests that it follows a normal distribution, which is typically symmetrical, with most data points clustering around the mean. This characteristic is useful for statistical analysis, helping to apply techniques like z-scores and standard deviation. Understanding the shape of the data can assist in deriving insights about the average performance and variability within this sample.
Transcribed Image Text:**Data Set Analysis** Use the following data set: **40, 33, 77, 12, 23, 56, 23, 19, 29** (in minutes). Assume the data is approximately bell-shaped and is a sample. In this dataset, each number represents a time duration recorded in minutes. The assumption that the data is "approximately bell-shaped" suggests that it follows a normal distribution, which is typically symmetrical, with most data points clustering around the mean. This characteristic is useful for statistical analysis, helping to apply techniques like z-scores and standard deviation. Understanding the shape of the data can assist in deriving insights about the average performance and variability within this sample.
**Hypothesis Testing for Mean (\( \mu \))**

**Objective:** 
- Test the claim that the population mean (\( \mu \)) is equal to 15.
- Sketch the distribution indicating the Critical Value.
- Evaluate the Test Statistic.
- Provide the p-value.

**Steps:**

1. **State the Hypotheses:**
   - Null Hypothesis (\( H_0 \)): \( \mu = 15 \)
   - Alternative Hypothesis (\( H_a \)): \( \mu \neq 15 \)

2. **Select the Significance Level (\( \alpha \)):**
   - Common choices for \( \alpha \) are 0.05, 0.01, etc.

3. **Sketch the Distribution:**
   - Draw a normal distribution curve.
   - Mark the Critical Value(s) on the curve.
   - The Critical Value delimiters will depend on your significance level.

4. **Calculate the Test Statistic:**
   - Use the formula for the test statistic (e.g., Z or T) based on the sample data.
   - Plug in your sample mean, population mean, standard deviation, and sample size into the formula.

5. **Determine the p-value:**
   - Locate the Test Statistic on the standard normal distribution.
   - The p-value is the probability that the test statistic is as extreme as, or more extreme than, the observed value under the null hypothesis.

6. **Make a Decision:**
   - If the p-value < \( \alpha \), reject the null hypothesis (\( H_0 \)).
   - If the p-value ≥ \( \alpha \), do not reject the null hypothesis (\( H_0 \)).

**Conclusion:**
- Based on the results, conclude whether there is sufficient evidence to reject the claim that \( \mu = 15 \).

**Visual Explanation:**
- A graphical representation can help visualize where the test statistic and critical values lie in relation to the significance level on the curve.
Transcribed Image Text:**Hypothesis Testing for Mean (\( \mu \))** **Objective:** - Test the claim that the population mean (\( \mu \)) is equal to 15. - Sketch the distribution indicating the Critical Value. - Evaluate the Test Statistic. - Provide the p-value. **Steps:** 1. **State the Hypotheses:** - Null Hypothesis (\( H_0 \)): \( \mu = 15 \) - Alternative Hypothesis (\( H_a \)): \( \mu \neq 15 \) 2. **Select the Significance Level (\( \alpha \)):** - Common choices for \( \alpha \) are 0.05, 0.01, etc. 3. **Sketch the Distribution:** - Draw a normal distribution curve. - Mark the Critical Value(s) on the curve. - The Critical Value delimiters will depend on your significance level. 4. **Calculate the Test Statistic:** - Use the formula for the test statistic (e.g., Z or T) based on the sample data. - Plug in your sample mean, population mean, standard deviation, and sample size into the formula. 5. **Determine the p-value:** - Locate the Test Statistic on the standard normal distribution. - The p-value is the probability that the test statistic is as extreme as, or more extreme than, the observed value under the null hypothesis. 6. **Make a Decision:** - If the p-value < \( \alpha \), reject the null hypothesis (\( H_0 \)). - If the p-value ≥ \( \alpha \), do not reject the null hypothesis (\( H_0 \)). **Conclusion:** - Based on the results, conclude whether there is sufficient evidence to reject the claim that \( \mu = 15 \). **Visual Explanation:** - A graphical representation can help visualize where the test statistic and critical values lie in relation to the significance level on the curve.
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