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) 1. What are the null and alternative hypotheses for this analysis? (hint: there should be 4 sets)2. Is the model as a whole significant? How do you know? ## Omnibus Tests of Model Coefficients- **Chi-square:** 13.639- **df:** 3- **Sig.:** .003## Model Summary- **-2 Log likelihood:** 80.640- **Cox & Snell R Square:** .127- **Nagelkerke R Square:** .209**Note:** Estimation terminated at iteration number 5 because parameter estimates changed by less than .001.## Hosmer and Lemeshow Test- **Chi-square:** 3.670- **df:** 8- **Sig.:** .886## Variables in the Equation| Variable | B | S.E. | Wald | df | Sig. | Exp(B) | 95% C.I. for EXP(B) Lower | Upper ||----------|---|------|------|----|------|--------|---------------------------|-------|| **head circumference** | -.164 | .124 | 1.753 | 1 | .186 | .849 | .666 | 1.082 || **systolic blood pressure** | -.054 | .030 | 3.322 | 1 | .068 | .948 | .894 | 1.004 || **baby's gender(1)** | 1.186 | .635 | 3.492 | 1 | .062 | 3.275 | .944 | 11.368 || **Constant** | 4.377 | 2.973 | 2.167 | 1 | .141 | 79.617 | | |**Note:** Variable(s) entered on step 1: head circumference, systolic blood pressure, baby's gender.## Gender Coding:- **Male:** 1- **Female:** 0

The variables will be able to predict haemorrhage if each variable is dependent on the response…

Answered: he data set hemorrhage.sav contains a…

MATLAB: An Introduction with Applications

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ISBN:9781119256830

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)

1. What are the null and alternative hypotheses for this analysis? (hint: there should be 4 sets)

2. Is the model as a whole significant? How do you know?

## Omnibus Tests of Model Coefficients
- **Chi-square:** 13.639
- **df:** 3
- **Sig.:** .003

## Model Summary
- **-2 Log likelihood:** 80.640
- **Cox & Snell R Square:** .127
- **Nagelkerke R Square:** .209

**Note:** Estimation terminated at iteration number 5 because parameter estimates changed by less than .001.

## Hosmer and Lemeshow Test
- **Chi-square:** 3.670
- **df:** 8
- **Sig.:** .886

## Variables in the Equation
| Variable | B | S.E. | Wald | df | Sig. | Exp(B) | 95% C.I. for EXP(B) Lower | Upper |
|----------|---|------|------|----|------|--------|---------------------------|-------|
| **head circumference** | -.164 | .124 | 1.753 | 1 | .186 | .849 | .666 | 1.082 |
| **systolic blood pressure** | -.054 | .030 | 3.322 | 1 | .068 | .948 | .894 | 1.004 |
| **baby's gender(1)** | 1.186 | .635 | 3.492 | 1 | .062 | 3.275 | .944 | 11.368 |
| **Constant** | 4.377 | 2.973 | 2.167 | 1 | .141 | 79.617 | | |

**Note:** Variable(s) entered on step 1: head circumference, systolic blood pressure, baby's gender.

## Gender Coding:
- **Male:** 1
- **Female:** 0

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