2. A doctor studies sets of fraternal twins, where one newborn is a boy, and the other is a girl. She believes that of these twins, one gender may be heavier than the other, but she's not sure which. Here is the data, with the weight of each baby (in ounces) Boy 84 72 73 41 85 91 72 67 58 74 54 62 71 48 91 Girl 82 65 77 39 71 87 80 60 55 80 46 57 66 48 83 Can the doctor say that there is any difference in the weights of the boy and girl in a set of twins? (Use a = .01) Type of Test: Null Hypothesis: Alternate Hypothesis: SE: Test Statistic: P-value: Decision: Sentence:

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### Hypothesis Testing in Fraternal Twins Birth Weights

**Scenario:**
A doctor studies sets of fraternal twins, where one newborn is a boy, and the other is a girl. She believes that of these twins, one gender may be heavier than the other, but she's not sure which. Below is the data, showing the weight of each baby in ounces.

#### Weights Data
- **Boy:** 84, 72, 73, 41, 85, 91, 72, 67, 58, 74, 54, 62, 71, 48, 91 
- **Girl:** 82, 65, 77, 39, 71, 87, 80, 60, 55, 80, 46, 57, 66, 48, 83

**Question:**
Can the doctor conclude that there is any difference in the weights of the boy and girl in a set of twins? (Use α = 0.01)

**Steps for Statistical Testing:**

1. **Type of Test:**
   - Two-sample t-test (independent)

2. **Null Hypothesis (H0):**
   - There is no difference in the weights of boys and girls in a set of fraternal twins.
   - \( H_0: \mu_{\text{boy}} = \mu_{\text{girl}} \)

3. **Alternate Hypothesis (H1):**
   - There is a difference in the weights of boys and girls in a set of fraternal twins.
   - \( H_1: \mu_{\text{boy}} \neq \mu_{\text{girl}} \)

4. **SE:**
   - Standard Error calculation (details to be filled after performing calculations).

5. **Test Statistic:**
   - Calculation of the t-statistic (details to be filled after performing calculations).

6. **P-value:**
   - Determine the p-value from the t-distribution.

7. **Decision:**
   - Compare the p-value to the significance level (α = 0.01) to accept or reject the null hypothesis.

8. **Sentence:**
   - Conclude based on the decision whether there is a significant difference in the weights of the boys and girls in a set of fraternal twins or not.

### Explanation:
This process involves statistical analysis to
Transcribed Image Text:### Hypothesis Testing in Fraternal Twins Birth Weights **Scenario:** A doctor studies sets of fraternal twins, where one newborn is a boy, and the other is a girl. She believes that of these twins, one gender may be heavier than the other, but she's not sure which. Below is the data, showing the weight of each baby in ounces. #### Weights Data - **Boy:** 84, 72, 73, 41, 85, 91, 72, 67, 58, 74, 54, 62, 71, 48, 91 - **Girl:** 82, 65, 77, 39, 71, 87, 80, 60, 55, 80, 46, 57, 66, 48, 83 **Question:** Can the doctor conclude that there is any difference in the weights of the boy and girl in a set of twins? (Use α = 0.01) **Steps for Statistical Testing:** 1. **Type of Test:** - Two-sample t-test (independent) 2. **Null Hypothesis (H0):** - There is no difference in the weights of boys and girls in a set of fraternal twins. - \( H_0: \mu_{\text{boy}} = \mu_{\text{girl}} \) 3. **Alternate Hypothesis (H1):** - There is a difference in the weights of boys and girls in a set of fraternal twins. - \( H_1: \mu_{\text{boy}} \neq \mu_{\text{girl}} \) 4. **SE:** - Standard Error calculation (details to be filled after performing calculations). 5. **Test Statistic:** - Calculation of the t-statistic (details to be filled after performing calculations). 6. **P-value:** - Determine the p-value from the t-distribution. 7. **Decision:** - Compare the p-value to the significance level (α = 0.01) to accept or reject the null hypothesis. 8. **Sentence:** - Conclude based on the decision whether there is a significant difference in the weights of the boys and girls in a set of fraternal twins or not. ### Explanation: This process involves statistical analysis to
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