This is an interpretation question. I have run a model with a binary dependent variable and both binary and non binary independent variables.
For discussion's sake, lets say that my dependent variable is the passing of a class. 0 if a student fails, 1 if they pass. Among the variables, one is "attended study hall at least once" where 0 is if they did not and 1 is if they attended at least once. The coefficient is .67683 and is statistically significant. I need to articulate how this improves the probability of passing the class when all else is held constant and of course, to do that, I need to understand it myself and I don't.
I took e ^ .67683 and got a value of 1.96763. When I subtract 1, I obviously get .96763. Am I to understand that this is telling me that students who attend study hall are 96.8% more likely to pass than those who don't?
For discussion's sake, lets say that my dependent variable is the passing of a class. 0 if a student fails, 1 if they pass. Among the variables, one is "attended study hall at least once" where 0 is if they did not and 1 is if they attended at least once. The coefficient is .67683 and is statistically significant. I need to articulate how this improves the probability of passing the class when all else is held constant and of course, to do that, I need to understand it myself and I don't.
I took e ^ .67683 and got a value of 1.96763. When I subtract 1, I obviously get .96763. Am I to understand that this is telling me that students who attend study hall are 96.8% more likely to pass than those who don't?
