The data set hemorrhage.sav contains a sample of 100 low birth weight infants born in Boston, Massachusetts. Germinal matrix hemorrhage (gmh) is a dichotomos variable that coded as 1 if the baby had a hemorrhage and 0 if the baby did not have a hemorrhage. You wish to know if head circumference, systolic blood pressure (sbp), and gender predicts hemorrhage. (See the attached Image) Determine the significance of the independent variables in the model, provide the test statistics and p-values to support your response.
The data set hemorrhage.sav contains a sample of 100 low birth weight infants born in Boston, Massachusetts. Germinal matrix hemorrhage (gmh) is a dichotomos variable that coded as 1 if the baby had a hemorrhage and 0 if the baby did not have a hemorrhage. You wish to know if head circumference, systolic blood pressure (sbp), and gender predicts hemorrhage. (See the attached Image) Determine the significance of the independent variables in the model, provide the test statistics and p-values to support your response.
MATLAB: An Introduction with Applications
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Author:Amos Gilat
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The data set hemorrhage.sav contains a sample of 100 low birth weight infants born in Boston, Massachusetts. Germinal matrix hemorrhage (gmh) is a dichotomos variable that coded as 1 if the baby had a hemorrhage and 0 if the baby did not have a hemorrhage.
You wish to know if head circumference, systolic blood pressure (sbp), and gender predicts hemorrhage. (See the attached Image)
- Determine the significance of the independent variables in the model, provide the test statistics and p-values to support your response.
![### Logistic Regression Analysis Summary
#### Omnibus Tests of Model Coefficients
- **Chi-square:** 13.639
- **Degrees of Freedom (df):** 3
- **Significance (Sig.):** .003
These values suggest that the model, including steps, blocks, and overall, is statistically significant.
#### Model Summary
- **-2 Log likelihood:** 80.640
- **Cox & Snell R Square:** .127
- **Nagelkerke R Square:** .209
Note:
- Estimation stopped at iteration 5 because parameter estimates changed by less than .001, indicating stability in the model estimates.
#### Hosmer and Lemeshow Test
- **Chi-square:** 3.670
- **Degrees of Freedom (df):** 8
- **Significance (Sig.):** .886
A high p-value from this test suggests a good fit of the model to the observed data.
#### Variables in the Equation
1. **Head Circumference**
- B: -.164
- S.E.: .124
- Wald: 1.753
- Sig.: .186
- Exp(B): .849
- 95% Confidence Interval (C.I.) for Exp(B): [.666, 1.082]
2. **Systolic Blood Pressure**
- B: -.054
- S.E.: .030
- Wald: 3.322
- Sig.: .068
- Exp(B): .948
- 95% C.I. for Exp(B): [.894, 1.004]
3. **Baby's Gender (Male = 1, Female = 0)**
- B: 1.186
- S.E.: .635
- Wald: 3.492
- Sig.: .062
- Exp(B): 3.275
- 95% C.I. for Exp(B): [.944, 11.368]
4. **Constant**
- B: 4.377
- S.E.: 2.973
- Wald: 2.167
- Sig.: .141
- Exp(B): 79.617
This table provides the logistic regression coefficients for the variables tested, including head circumference, systolic blood pressure, and baby's gender. The Exp(B](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F39a4cf42-6700-4f78-b168-8529352bd644%2F0f295d5f-77ce-4e49-b23b-500a94b138f8%2F9kgtbco_processed.png&w=3840&q=75)
Transcribed Image Text:### Logistic Regression Analysis Summary
#### Omnibus Tests of Model Coefficients
- **Chi-square:** 13.639
- **Degrees of Freedom (df):** 3
- **Significance (Sig.):** .003
These values suggest that the model, including steps, blocks, and overall, is statistically significant.
#### Model Summary
- **-2 Log likelihood:** 80.640
- **Cox & Snell R Square:** .127
- **Nagelkerke R Square:** .209
Note:
- Estimation stopped at iteration 5 because parameter estimates changed by less than .001, indicating stability in the model estimates.
#### Hosmer and Lemeshow Test
- **Chi-square:** 3.670
- **Degrees of Freedom (df):** 8
- **Significance (Sig.):** .886
A high p-value from this test suggests a good fit of the model to the observed data.
#### Variables in the Equation
1. **Head Circumference**
- B: -.164
- S.E.: .124
- Wald: 1.753
- Sig.: .186
- Exp(B): .849
- 95% Confidence Interval (C.I.) for Exp(B): [.666, 1.082]
2. **Systolic Blood Pressure**
- B: -.054
- S.E.: .030
- Wald: 3.322
- Sig.: .068
- Exp(B): .948
- 95% C.I. for Exp(B): [.894, 1.004]
3. **Baby's Gender (Male = 1, Female = 0)**
- B: 1.186
- S.E.: .635
- Wald: 3.492
- Sig.: .062
- Exp(B): 3.275
- 95% C.I. for Exp(B): [.944, 11.368]
4. **Constant**
- B: 4.377
- S.E.: 2.973
- Wald: 2.167
- Sig.: .141
- Exp(B): 79.617
This table provides the logistic regression coefficients for the variables tested, including head circumference, systolic blood pressure, and baby's gender. The Exp(B
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