Dear Mohamed
Your dataset did not make it through, the list strips most attachments.
In my area of application I would be suspicious that such an odds ratio
was the result of a data error or my misunderstanding of the underlying
science. You are probably in the best position to judge both of these in
your area.
Michael
On 06/02/2015 07:42, Mohamed Farah wrote:
I have run a logit regression with two categorical variables (with 0 and 1) as
the values. i.e. payment (1) / non-payment(0) on profit (profitable =1,
non-profitable=0) on 375 entities. Here is the result from R:
divgress <-glm(Div~PRFD, family=binomial(link="logit"), data=divs)
summary(divgress)
Call:
glm(formula = Div ~ PRFD, family = binomial(link = "logit"),
data = divs)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.6765 -0.2626 0.7502 0.7502 2.6017
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.3499 0.7194 -4.656 3.22e-06 ***
PRFD 4.4738 0.7311 6.119 9.41e-10 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 491.84 on 376 degrees of freedom
Residual deviance: 371.78 on 375 degrees of freedom
AIC: 375.78
Number of Fisher Scoring iterations: 6
My question is that the coefficient of the independent variable (log-odds) at
4.4738 is difficult to interpret. I have obtained the exponent of the
coefficient below and as the result of 87.69.. shown below shows, the number
is high which makes suspicious that there is something not working right.
exp(coef(divgress))
(Intercept) PRFD
0.03508772 87.69230769
The dataset is attached. I appreciate your help.
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