# Logistic Regression Coefficient Intrepretation - Impact On Churn

I am using logistic to see the relationship between churn and minute seen.

I get something like this

> summary(log_model_test )

Call:
glm(formula = isChurn ~ avg_minutes_seen, family = binomial(link = "logit"),
data = train_churn_2)

Deviance Residuals:
Min       1Q   Median       3Q      Max
-0.7142  -0.5852  -0.4712  -0.2987   4.8138

Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept)    -1.2361998  0.0089931 -137.46   <2e-16 ***
avg_minutes_seen -0.0005472  0.0000062  -88.26   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 209628  on 285696  degrees of freedom
Residual deviance: 198453  on 285695  degrees of freedom
AIC: 198457

Number of Fisher Scoring iterations: 6


Now, I am trying to interpret results; however, I don't know what is the correct way to express this relationship. Which is the right way to interpret the result? I just want to say how much impact minutes seen has on churn.

This fitted model says that, the odds of churning (churn = 1) over the odds of not churning (churn = 0) is exp(-0.0005472) = 0.9994398

1- For a one-unit increase in the average minute seen, the expected change in log odds is 0.0005604

2- For every one unit change in an average minute seen, the log odds of non-churn (versus churn) increases by 0.999766

3- So we can say for a one-unit increase in an average minute seen, we expect to see about (1 - 0.9997661) x 100 ) = 0.05602% decrease in the odds of being in churn class.