I need help with all of them plz. ### Blood type and COVID-19 susceptibilityA 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.Round all calculated values in this problem to 4 decimal places.#### Blood Type Distribution in Wuhan Population 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 in the table below:**| Blood Type | Expected Value ||------------|----------------|| Type A | || Type B | || Type AB | || Type O | |**2. 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 not** due to chance. **Alternative Hypothesis (H₁):** The distribution of blood type of COVID-19 patients is **different from** the population. Any observed difference **is** due to chance. **Chi-Square Test for Blood Type Distribution in COVID-19 Patients****Objective:**To determine if the distribution of blood types among COVID-19 patients in Wuhan is different from that of the general population.**Procedure:**2. **Hypothesis Formulation:** - Null Hypothesis ($ H_0 $): The distribution of blood type of COVID-19 patients is the same as the population. Any observed difference is not due to chance. - Alternative Hypothesis ($ H_A $): The distribution of blood type of COVID-19 patients is different from the population. Any observed difference is due to chance.3. **Primary Variable Contribution:** - Identify which blood type (A or O) contributes more to the test statistic. - Options: - A. type A - B. type O4. **Calculate the Test Statistic:** - Chi-square test statistic ($ \chi^2 $) calculation input field. - $ \chi^2 = $ [Input Field]5. **Degrees of Freedom:** - State the degrees of freedom for this test. - $ df = $ [Input Field]6. **Calculate the p-value:** - Input field to calculate the p-value for this test. - $ p = $ [Input Field]7. **Result Interpretation:** - Based on the above p-value, select the strength of evidence against the null hypothesis. - Options: - [Dropdown] Strong evidenceThis set of data aids in understanding how to apply statistical methods to compare categorical data distributions, specifically using chi-square tests to evaluate observed vs. expected frequency distributions.

The Chi-square test statistic value is obtained using the formula,

coronavirus and patients' blood type. The population in Wuhan has a blood type distribution as shown in the table below. The researchers categorized Snsamp patients who had contracted coronavirus by blood type. Round all calculated values in this problem to 4 decimal places. Blood Type Population Percentage COVID-19 patients Туре А 33% 115 Туре В 24% 101 Туре АВ 9% 41 Туре О 34% 118 Total 100% 375 1. Enter the expected values for the hypothesis test in the table below: Blood Type Expected Value Type A Туре В Type AB Туре О 2. 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. Ho: The distribution of blood type of COVID-19 patients is the same as the population. Any observed difference is not v due to chance. HA: The distribution of blood type of COVID-19 patients is different from v the population. Any observed difference is due to

coronavirus and patients' blood type. The population in Wuhan has a blood type distribution as shown in the table below. The researchers categorized Snsamp patients who had contracted coronavirus by blood type. Round all calculated values in this problem to 4 decimal places. Blood Type Population Percentage COVID-19 patients Туре А 33% 115 Туре В 24% 101 Туре АВ 9% 41 Туре О 34% 118 Total 100% 375 1. Enter the expected values for the hypothesis test in the table below: Blood Type Expected Value Type A Туре В Type AB Туре О 2. 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. Ho: The distribution of blood type of COVID-19 patients is the same as the population. Any observed difference is not v due to chance. HA: The distribution of blood type of COVID-19 patients is different from v the population. Any observed difference is due to

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Estimation

A new framework of data analysis where the new statistical methods are used for interpreting the results and analyzing the data is known as estimation in statistics.

Question

I need help with all of them plz.

### Blood type and COVID-19 susceptibility

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.

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

#### Blood Type Distribution in Wuhan Population 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 in the table below:**

| Blood Type | Expected Value |
|------------|----------------|
| Type A | |
| Type B | |
| Type AB | |
| Type O | |

**2. 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 not** due to chance.

**Alternative Hypothesis (H₁):** The distribution of blood type of COVID-19 patients is **different from** the population. Any observed difference **is** due to chance.

**Chi-Square Test for Blood Type Distribution in COVID-19 Patients**

**Objective:**
To determine if the distribution of blood types among COVID-19 patients in Wuhan is different from that of the general population.

**Procedure:**

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

3. **Primary Variable Contribution:**
- Identify which blood type (A or O) contributes more to the test statistic.
- Options:
- A. type A
- B. type O

4. **Calculate the Test Statistic:**
- Chi-square test statistic ($ \chi^2 $) calculation input field.
- $ \chi^2 = $ [Input Field]

5. **Degrees of Freedom:**
- State the degrees of freedom for this test.
- $ df = $ [Input Field]

6. **Calculate the p-value:**
- Input field to calculate the p-value for this test.
- $ p = $ [Input Field]

7. **Result Interpretation:**
- Based on the above p-value, select the strength of evidence against the null hypothesis.
- Options:
- [Dropdown] Strong evidence

This set of data aids in understanding how to apply statistical methods to compare categorical data distributions, specifically using chi-square tests to evaluate observed vs. expected frequency distributions.

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