# How to interpret coefficients from logistic regression?

I ran a logistic regression (statsmodel) on my data with 60 features using the below code

import statsmodels.api as sm
logit_model=sm.Logit(y_train,X_train_std)
result=logit_model.fit()
print(result.summary())


I was able to see that few variables had negative coefficient and few had positive coefficients.

Am I right to understand that irrespective of sign of coefficients, all the below variables are significant predictors that influence the outcome?

Or does negative coefficient mean they don't have any influence on the model outcome? But p-value is significant. Am a bit confused. Can you help in simple terms please

The below output shows the records whose p-values were less than 0.05

• the coefficient magnitude is what's relevant, the sign is just telling you whether the predicted probability grows when the feature grows (positive coefficient) or decreases when the feature grows (negative coefficient). Dec 18 '19 at 7:44
• So, since I am predcting a binary outcome (disease occur or not), we need to have both positive and negative coefficient variables (with signifncant p-values). Am I right? Dec 18 '19 at 7:50
• not necessarily, it's perfectly normal to have all positive, all negative, or both positive and negative coefficients. Dec 18 '19 at 10:26
• Yeah, so positive coefficients indicate majorly influencing one class while negative coefficients indicate majorly influencing the other class. Right? Dec 18 '19 at 10:28
• influencing is an awkward word choice but yes. Dec 18 '19 at 10:55

• By defining what do you mean? I have defined people who have disease as 1 and don't have disease as 0 Dec 18 '19 at 9:18