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I am calculating the odd ratio of logistic regression (using statsmodel of Python). I have one independent variable (i.e. process type: faulty (1) or non-faulty (2) and one dependent variable (i.e. process-time: late (0) or on-time (1)). I calculated the odd ratio at C.I 95% using logistic regression (I used statsmodel of Python). I got the following value:

                    5%        95%      Odds Ratio
Process type    1.431001    1.541844    1.485389

I interpreted the above odds ratio as "Increasing from 1 to 2 for Process type (i.e. going from a faulty to non-faulty) is associated with an increased in the odds by 48% of completing the process on time."

My first question: is my interpretation correct?

As the independent variable is nominal in nature, do I need to calculate the separate odd ratio for each value type of independent variable (i.e. one odd ratio for faulty values and one add ratio of non-faulty values)? My understanding is that as I am using logistic regression to calculate odds ratio so I should be using an independent variable that has at least two types of nominal variable values. I can not calculate for a single type of nominal value.

Could anyone shed some light on it?

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  • $\begingroup$ just confirming, non-faulty is your control group? $\endgroup$
    – jros
    Jul 22, 2021 at 18:01
  • $\begingroup$ Non-faulty is one of the value type of independent variable. What do you mean by control group??? $\endgroup$ Jul 22, 2021 at 19:26
  • $\begingroup$ For the interpretation, are you comparing faulty to non-faulty or non-faulty to faulty? $\endgroup$
    – jros
    Jul 22, 2021 at 19:32
  • $\begingroup$ @jros non-faulty to faulty $\endgroup$ Jul 22, 2021 at 20:00

1 Answer 1

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The interpretation should be adjusted slightly to something more similar to: The odds of a non-faulty process experiencing an on-time process-time are 48% greater than the odds of a faulty process experiencing an on-time process-time.

In order to calculate the odds ratio for faulty process, just flip the odds ratio (1/1.485).

The coefficients of a logistic regression rely on comparison, so even continuous, numerical independent variables have an comparative interpretation

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