A coefficient describes the size of the contribution of that predictor; a large coefficient indicates that the variable strongly influences the probability of that outcome, while a near-zero coefficient indicates that variable has little influence on the probability of that outcome. A positive sign indicates that the explanatory variable increases the probability of the outcome, while a negative sign indicates that the variable decreases the probability of that outcome. A confidence interval for each parameter shows the uncertainty in the estimate.
When the model contains categorical variables, the interpretation of the coefficients is more complex. For each term involving a categorical variable, a number of dummy predictor variables are created to predict the effect of each different level. There are different ways to code the predictors for a categorical variable, the most common method in logii/probit regression is called reference cell coding or dummy coding. In reference cell coding, the first category acts as a baseline, and you can interpret the other coefficients as an increase or decrease over the baseline category.
From the Statistical Reference Guide for Analyse-it 6.24.0: https://analyse-it.com/docs/user-guide/fit-model/logistic/parameter-estimates
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