Interpreting a Logit regression result

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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Michael
http://www.dewey.myzen.co.uk