Interpreting coefficients in a logistic regression - Cross Validated

Logistic regression interpretation of coefficients. I have created a model to predict how likely a top-funded cleantech company is to go through an exit (whether IPO or a buyout), depending on the type of finance they have received in the past. I have a binary exit variable as my dependent variable; factor variables for country and subsector ; numeric variables for year and number of rounds ; and binary variables for different investment types . While the variables showed high level of significance, I am not sure how to interpret the coefficients. How do I get the odds/probabilities for my variables in a model like this, where I have multiple variables, and some variables have multiple factor levels? The format is consistent with [R], so I'll go with this assumption. Presumably you have the regression saved as an object of the type: You probably should be able to now get the following: exp(cbind(Odd_Ratio = coef(fit), confint(fit))) . Given that $e^0=1$, the confidence intervals should not contain $1$ if the coefficient is significant. We see that loan is significant with a coefficient of 1.44755 . The exp(1.44755) = 4.2526 indicates that getting a loan as means of financing makes your odds of exit approximately $4$ times higher than the odds you would have with your "baseline" financing system as it is ordered by [R] (alphabetical arrangement of the levels with the first one typically omitted. grant , loan , loan_guarantee ). As for continuous variables, such as year the interpretation would be along the lines of exp(-0.18171) = 0.8338 , implying a decrease of the odds of exit of $\sim 20\%$ per year. Now to turn odds into probabilities you use the formula: $\displaystyle \frac \,$ to calculate the increased (or decreased) probability of exit .