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. (See the attached image)You wish to know if gestational age, systolic blood pressure (sbp), and gender predicts hemorrhage. What are the null and alternative hypotheses for this analysis? (hint: there should be 4 sets) Is the model as a whole significant? How do you know?Regardless if the variables are significant interpret the odds ratio for gender, sbp, and gestational age. Model Summary-2 LoglikelihoodCox & Snell RSquareNagelkerkeR SquareStep176.165.166.271a. Estimation terminated at iteration number 6because parameter estimates changed by lessthan .00i.Hosmer and Lemeshow TestStepChi-squaredfSig.110.4188.237Omnibus Tests of Model CoefficientsChi-squaredfSig.Step 1 Step18.1143.000Block18.1143.000Model18.1143.000Variables in the Equation95% C.I.for EXP(B)ВS.E.WalddfSig.Exp(B)LowerUpperStep 1* systolic blood pressure-.045.0292.4371.119.956.9031.012baby's gender(1)1.215.6483.5191.0613.370.94711.995gestational age-.315.1345.5591.018.730.561.948Constant8.6163.6105.6961.017 5516.878a. Variable(s) entered on step 1: systolic blood pressure, baby's gender, gestational age.Baby gender:Male = 1Female= 0

1. The task is to test the association between the variables. Ho : β0=β1=β2=β3H1 : At least two of…

Answered: The data set hemorrhage.sav contains a…

MATLAB: An Introduction with Applications

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ISBN:9781119256830

Author:Amos Gilat

Publisher: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. (See the attached image)

You wish to know if gestational age, systolic blood pressure (sbp), and gender predicts hemorrhage.

What are the null and alternative hypotheses for this analysis? (hint: there should be 4 sets)
Is the model as a whole significant? How do you know?
Regardless if the variables are significant interpret the odds ratio for gender, sbp, and gestational age.

Model Summary
-2 Log
likelihood
Cox & Snell R
Square
Nagelkerke
R Square
Step
1
76.165
.166
.271
a. Estimation terminated at iteration number 6
because parameter estimates changed by less
than .00i.
Hosmer and Lemeshow Test
Step
Chi-square
df
Sig.
1
10.418
8
.237
Omnibus Tests of Model Coefficients
Chi-square
df
Sig.
Step 1 Step
18.114
3
.000
Block
18.114
3
.000
Model
18.114
3
.000
Variables in the Equation
95% C.I.for EXP(B)
В
S.E.
Wald
df
Sig.
Exp(B)
Lower
Upper
Step 1* systolic blood pressure
-.045
.029
2.437
1
.119
.956
.903
1.012
baby's gender(1)
1.215
.648
3.519
1
.061
3.370
.947
11.995
gestational age
-.315
.134
5.559
1
.018
.730
.561
.948
Constant
8.616
3.610
5.696
1
.017 5516.878
a. Variable(s) entered on step 1: systolic blood pressure, baby's gender, gestational age.
Baby gender:
Male = 1
Female= 0

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