3. Did blood type A or blood type O contribute more to the test statistic? OA. type A оB. type O 4. Calculate the test statistic. x? = 5. State the degrees of freedom for this test. df = 6. Calculate the p-value for this test. p = 7. Based on the above p-value, we have v ? evidence against the null hypothesis. extremely strong very strong Help Entering Answers strong nomo
3. Did blood type A or blood type O contribute more to the test statistic? OA. type A оB. type O 4. Calculate the test statistic. x? = 5. State the degrees of freedom for this test. df = 6. Calculate the p-value for this test. p = 7. Based on the above p-value, we have v ? evidence against the null hypothesis. extremely strong very strong Help Entering Answers strong nomo
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
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![**Chi-Square Test Exercise**
**3. Did blood type A or blood type O contribute more to the test statistic?**
- A. type A
- B. type O
**4. Calculate the test statistic.**
\[ \chi^2 = \]
**5. State the degrees of freedom for this test.**
\[ df = \]
**6. Calculate the p-value for this test.**
\[ p = \]
**7. Based on the above p-value, we have [dropdown menu with options:?] evidence against the null hypothesis.**
- extremely strong
- very strong
- strong
- some
[Dropdown menu is present here to select the strength of evidence against the null hypothesis.]
**Instructions:**
- To generate the test statistic (\[ \chi^2 \]), use the formula:
\[ \chi^2 = \sum \frac{(O_i - E_i)^2}{E_i} \]
where \( O_i \) is the observed frequency and \( E_i \) is the expected frequency.
- Degrees of freedom (\[ df \]) is calculated using:
\[ df = (r - 1) (c - 1) \]
where \( r \) is the number of rows and \( c \) is the number of columns in the contingency table.
- To find the p-value, refer to the Chi-Square distribution table using your calculated \[ \chi^2 \] value and the corresponding degrees of freedom.
[The guide "Help Entering Answers" is available for further assistance in entering responses.]](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F9896e9f0-21a4-41f5-9cfe-a9d2b9678883%2Fa004f927-43e8-4f01-ab30-bc1097934263%2F4vd659e_processed.png&w=3840&q=75)
Transcribed Image Text:**Chi-Square Test Exercise**
**3. Did blood type A or blood type O contribute more to the test statistic?**
- A. type A
- B. type O
**4. Calculate the test statistic.**
\[ \chi^2 = \]
**5. State the degrees of freedom for this test.**
\[ df = \]
**6. Calculate the p-value for this test.**
\[ p = \]
**7. Based on the above p-value, we have [dropdown menu with options:?] evidence against the null hypothesis.**
- extremely strong
- very strong
- strong
- some
[Dropdown menu is present here to select the strength of evidence against the null hypothesis.]
**Instructions:**
- To generate the test statistic (\[ \chi^2 \]), use the formula:
\[ \chi^2 = \sum \frac{(O_i - E_i)^2}{E_i} \]
where \( O_i \) is the observed frequency and \( E_i \) is the expected frequency.
- Degrees of freedom (\[ df \]) is calculated using:
\[ df = (r - 1) (c - 1) \]
where \( r \) is the number of rows and \( c \) is the number of columns in the contingency table.
- To find the p-value, refer to the Chi-Square distribution table using your calculated \[ \chi^2 \] value and the corresponding degrees of freedom.
[The guide "Help Entering Answers" is available for further assistance in entering responses.]
Transcribed Image Text:### Blood Type and COVID-19 Susceptibility
A group of researchers in Wuhan, China, investigated the relationship between contracting the novel coronavirus and patients' blood types. 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.
**Instructions**: Round all calculated values in this problem to 4 decimal places.
#### Blood Type Distribution and COVID-19 Patients
| 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** |
---
#### 1. Enter the Expected Values for the Hypothesis Test
Calculate the expected values based on the given population percentage for each blood type. Use the total number of COVID-19 patients (375) to determine these values.
| Blood Type | Expected Value |
|------------|----------------|
| Type A | |
| Type B | |
| Type AB | |
| Type O | |
---
#### 2. Null and Alternative Hypotheses
The researchers wonder if, in Wuhan, COVID-19 patients have a different distribution of blood types than the general population of Wuhan. Express this research question in terms of a null and alternative hypothesis.
**Null Hypothesis (\( H_0 \))**:
- The distribution of blood types 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 types of COVID-19 patients is **different from** the population. Any observed difference **is not** due to chance.
This setup helps in determining if there is a significant association between blood type and the likelihood of contracting COVID-19 or if any differences are merely due to randomness.
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