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Hi Paaveen,

 

I read your mail and have the following suggestions to offer.

 

Do stepwise selection procedure in logistic regression with all potential covariates. The procedure will eliminate any variable that is linearly correlated with others already in the model.

 

Your final variables selected through the stepwise selection criterion will be viable predictors without any collinearity. When doing stepwise selection, use alpha that is more conservative so that you have worst scenario included in variable selection.

 

 

Remember that your descriptive Statistics is just a stepping stone and not a conclusive stage of determining which variable goes into the model and which does not.

 

Mubashir 

 

From: A UK-based worldwide e-mail broadcast system mailing list [mailto:[log in to unmask]] On Behalf Of paaveen jeyaganth
Sent: Wednesday, May 25, 2016 10:31 AM
To: [log in to unmask]
Subject: logistic regression

 

Dear Allstat,

I am doing logistic regression 

before i do logistic regression. i did some descriptive statistic

 

death is outcome

N=450

covariate :- age (continuous), gender (2 group), HTN(2 group), PDM(2 group), 

                  PMI(2 group) , ACS(3 group), bif(4 group), GEN(2 group)

 

for descriptive statistics , i did chi-square test GEN is related to covariate

from that i found ACS and HTN are statistically significant to GEN,

 

when i do the backward logistic regression 

1) is it correct include ACS , HTN and GEN in the model

  or do i have to only include one of these above variable?

 

2)When i do the logistic regression i include all the variable in the 

  model final model include ACS and GEN

if this two variable are related to each others then there is collinearity  is present?

 

 

i really need step-wise advice what i am doing correct?

appreciate any advise, 

 

 

thanks 

paaveen

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