Hello!

Strugling a bit with statistics as a newbie.

I've got 2 factor variables in dataset:

acceptance (with 2 levels - accepted and rejected) and

timeslot (with 6 levels of different time bins).

I'm trying to do a regression model and to see in which timeslot the highest/lowest probability of acceptance.

So I'm doing my model as following:

hour_glm = glm(acceptance ~ timeslot , family="binomial", data = df)

summary(hour_glm)

Call:

glm(formula = acceptance ~ timeslot , family="binomial", data = df)

Deviance Residuals:

Min 1Q Median 3Q Max

-1.6124 -1.4993 0.8680 0.8680 0.9126

##
Coefficients:

Estimate Std. Error z value Pr(>|z|)

(Intercept) 0.98173 0.05762 17.037 < 2e-16 ***

timeslot2 -0.32109 0.08791 -3.652 0.00026 ***

timeslot3 -0.19961 0.06874 -2.904 0.00369 **

timeslot4 -0.25082 0.06587 -3.808 0.00014 ***

timeslot5 -0.19986 0.06691 -2.987 0.00282 **

timeslot6 -0.13346 0.07036 -1.897 0.05786 .

Signif. codes: 0 ‘* ’ 0.001 ‘’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

```
Null deviance: 21421 on 17248 degrees of freedom
```

Residual deviance: 21400 on 17243 degrees of freedom

AIC: 21412

## Number of Fisher Scoring iterations: 4

And then I'm stuck. I'm not very good managing factors. How can I see when is highest/lowest acceptance probability?

Also what's the best way 2 visualize when you have 2 levels in the dependent var. ?

Thank you!