6. Calculate the p-value for this test. p = 0.475687 7. Based on the above p-value, we have extremely strong evidence against the null hypothesis.

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---

### Blood Type and COVID-19 Susceptibility

**Introduction:**
A group of researchers in Wuhan, China, investigated the relationship between contracting the novel coronavirus and patients' blood type. The population in Wuhan has a blood type distribution as shown in the table below. The researchers categorized 375 patients who had contracted coronavirus by blood type.

**Population and Patient Distribution:**

| Blood Type | Population Percentage | COVID-19 Patients |
|------------|-----------------------|-------------------|
| Type A     | 33%                   | 115               |
| Type B     | 24%                   | 101               |
| Type AB    | 9%                    | 41                |
| Type O     | 34%                   | 118               |
| **Total**  | **100%**              | **375**           |


**Instructions:**
Round all calculated values in this problem to 4 decimal places.

---

**1. Calculate Expected Values**

Calculate the expected values for the hypothesis test. Enter these values in the table below:

| Blood Type | Expected Value |
|------------|----------------|
| Type A     | 123.75         |
| Type B     | 90             |
| Type AB    | 33.75          |
| Type O     | 127.5          |


**2. Hypothesis Testing**

The researchers wonder if, in Wuhan, COVID-19 patients have a different distribution of blood type than the general population of Wuhan. Express this research question in terms of a null and alternative hypothesis.

- **Null Hypothesis (H₀):** The distribution of blood type of COVID-19 patients is **the same as** the population. Any observed difference **is** due to chance.

- **Alternative Hypothesis (H₁):** The distribution of blood type of COVID-19 patients is **different from** the population. Any observed difference **is not** due to chance.

---

To proceed with the analysis, calculate the expected frequencies assuming the null hypothesis is true, then compare them with the observed frequencies using a chi-square test.

This data is critical for understanding any significant differences between the blood type distribution of COVID-19 patients and the general population, which can help in medical research and public health decisions.

Continue to practice with similar datasets to enhance your statistical analysis skills.

---
Transcribed Image Text:--- ### Blood Type and COVID-19 Susceptibility **Introduction:** A group of researchers in Wuhan, China, investigated the relationship between contracting the novel coronavirus and patients' blood type. The population in Wuhan has a blood type distribution as shown in the table below. The researchers categorized 375 patients who had contracted coronavirus by blood type. **Population and Patient Distribution:** | Blood Type | Population Percentage | COVID-19 Patients | |------------|-----------------------|-------------------| | Type A | 33% | 115 | | Type B | 24% | 101 | | Type AB | 9% | 41 | | Type O | 34% | 118 | | **Total** | **100%** | **375** | **Instructions:** Round all calculated values in this problem to 4 decimal places. --- **1. Calculate Expected Values** Calculate the expected values for the hypothesis test. Enter these values in the table below: | Blood Type | Expected Value | |------------|----------------| | Type A | 123.75 | | Type B | 90 | | Type AB | 33.75 | | Type O | 127.5 | **2. Hypothesis Testing** The researchers wonder if, in Wuhan, COVID-19 patients have a different distribution of blood type than the general population of Wuhan. Express this research question in terms of a null and alternative hypothesis. - **Null Hypothesis (H₀):** The distribution of blood type of COVID-19 patients is **the same as** the population. Any observed difference **is** due to chance. - **Alternative Hypothesis (H₁):** The distribution of blood type of COVID-19 patients is **different from** the population. Any observed difference **is not** due to chance. --- To proceed with the analysis, calculate the expected frequencies assuming the null hypothesis is true, then compare them with the observed frequencies using a chi-square test. This data is critical for understanding any significant differences between the blood type distribution of COVID-19 patients and the general population, which can help in medical research and public health decisions. Continue to practice with similar datasets to enhance your statistical analysis skills. ---
### Hypothesis Testing for Blood Type Distribution in COVID-19 Patients in Wuhan

**2. Null and Alternative Hypotheses**

The researchers question if the distribution of blood types in COVID-19 patients in Wuhan is different from that of the general population of Wuhan. This is expressed in terms of the following hypotheses: 

- **Null Hypothesis (\(H_0\))**: The distribution of blood type of COVID-19 patients is **the same as** the population. Any observed difference is **due to** chance.
- **Alternative Hypothesis (\(H_A\))**: The distribution of blood type of COVID-19 patients is **different from** the population. Any observed difference is **not due to** chance.

**3. Analysis of Contributions to the Test Statistic**

The research examines which blood type, A or O, contributes more to the test statistic. Based on the input:

- **Blood type O** is identified as contributing more to the test statistic.

**4. Calculating the Test Statistic**

The calculated chi-squared (\(\chi^2\)) test statistic value is:

\[ \chi^2 = 4.2283 \]

**5. Degrees of Freedom**

The degrees of freedom (df) for this test is given as:

\[ d_f = 3 \]

**6. Calculating the p-Value**

The p-value corresponding to the test statistic is:

\[ p = 0.47568 \]

**7. Conclusion Based on the p-Value**

With the given p-value, we state that:

- The evidence against the null hypothesis is **extremely strong**.

In conclusion, these statistical results are useful for determining whether the observed blood type distribution in COVID-19 patients is significantly different from that of the general population in Wuhan, providing critical insights into potential associations between blood type and COVID-19 infection.
Transcribed Image Text:### Hypothesis Testing for Blood Type Distribution in COVID-19 Patients in Wuhan **2. Null and Alternative Hypotheses** The researchers question if the distribution of blood types in COVID-19 patients in Wuhan is different from that of the general population of Wuhan. This is expressed in terms of the following hypotheses: - **Null Hypothesis (\(H_0\))**: The distribution of blood type of COVID-19 patients is **the same as** the population. Any observed difference is **due to** chance. - **Alternative Hypothesis (\(H_A\))**: The distribution of blood type of COVID-19 patients is **different from** the population. Any observed difference is **not due to** chance. **3. Analysis of Contributions to the Test Statistic** The research examines which blood type, A or O, contributes more to the test statistic. Based on the input: - **Blood type O** is identified as contributing more to the test statistic. **4. Calculating the Test Statistic** The calculated chi-squared (\(\chi^2\)) test statistic value is: \[ \chi^2 = 4.2283 \] **5. Degrees of Freedom** The degrees of freedom (df) for this test is given as: \[ d_f = 3 \] **6. Calculating the p-Value** The p-value corresponding to the test statistic is: \[ p = 0.47568 \] **7. Conclusion Based on the p-Value** With the given p-value, we state that: - The evidence against the null hypothesis is **extremely strong**. In conclusion, these statistical results are useful for determining whether the observed blood type distribution in COVID-19 patients is significantly different from that of the general population in Wuhan, providing critical insights into potential associations between blood type and COVID-19 infection.
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