# Odd ratio for binomial variable values

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?

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