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 CoefficientsChi-squaredfSig.Step 1 Step13.6393.003Block13.6393.003Model13.6393.003Model Summary-2 LoglikelihoodCox & Snell RSquareNagelkerkeR SquareStep180.640.127.209a. Estimation terminated at iteration number 5because parameter estimates changed by lessthan .001.Hosmer and Lemeshow TestStepChi-squaredfSig.13.6708.886Variables in the Equation95% C.I.for EXP(B)ВS.E.WalddfSig.Exp(B)LowerUpperStep 1 head circurmference-.164.1241.7531.186.849.6661.082systolic blood pressure-.054.0303.3221.068.948.8941.004baby's gender(1)1.186.6353.4921.0623.275.94411.368Constant4.3772.9732.1671.14179.617a. Variable(s) entered on step 1: head circurmference, systolic blood pressure, baby's gender.Gender:Male = 1Female = 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

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Related questions

Question

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
df
Sig.
Step 1 Step
13.639
3
.003
Block
13.639
3
.003
Model
13.639
3
.003
Model Summary
-2 Log
likelihood
Cox & Snell R
Square
Nagelkerke
R Square
Step
1
80.640
.127
.209
a. Estimation terminated at iteration number 5
because parameter estimates changed by less
than .001.
Hosmer and Lemeshow Test
Step
Chi-square
df
Sig.
1
3.670
8
.886
Variables in the Equation
95% C.I.for EXP(B)
В
S.E.
Wald
df
Sig.
Exp(B)
Lower
Upper
Step 1 head circurmference
-.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
a. Variable(s) entered on step 1: head circurmference, systolic blood pressure, baby's gender.
Gender:
Male = 1
Female = 0

Expert Solution

Trending now

This is a popular solution!

Step by step

Solved in 2 steps

SEE SOLUTION Check out a sample Q&A here

Knowledge Booster

$Chi-squared Tests$

Learn more about

Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, statistics and related others by exploring similar questions and additional content below.

Similar questions