# Interpreting odds in a logistic regression

I am having some problems interpreting the odds. I run a logistic regression for an out come 'Yes' or 'no'. My reference category is 'No'.

I have 2 variables and this are the log(odds) and the odds: Variable A -> It is a integer with values 2 to 80. logodds: -0.014078787; odds: 0.9860199 Variable B -> It is a integer with values 1 to 30. logodds: 0.214099984; odds: 1.2387465

What is a correct statement to interpret these odds?

And how do I transform this in real probabilities? I would like to say something like: Every extra point of variable A causes an decrease of x in the probabilities of going from 'No' to 'Yes' If I do exp(coef(model)) /(1+exp(coef(model))) for Variable A I obtain 0.49. This is the probability right? Is it correct to say that each point of variable A makes the probability go down 0.01%

Lets say your baseline odds ($\exp(constant)$) is .8. This would mean that there are 0.8 people who say yes for every person who says no when all explanatory variables are 0. Your odds ratio for A says that a unit increase in A is associated with a 1% decrease in the odds of saying yes, while the odds ratio for B says that a unit increas in B is associated with a 24% increase in the odds of saying yes.