That is, the odds ratio is the natural log base e raised to the power of b. There will be a b coefficient for each category of the categorical variable, except the reference category. The odds of a person in the given category of an independent variable (the category corresponding to b) also being associated with the reference category of the dependent variable (usually 1, corresponding to an event happening; or to the highest dependent category when the dependent has > 2 values; though the researcher can set the dependent reference category as desired) is Exp(b) times the odds of a person in the reference category of the independent variable, controlling for other variables in the model. Also, when the independent increases one unit, the odds of the dependent (usually 1 = event happening) increase by a factor of x.

If the 95% confidence interval on the odds ratio includes the value of 1.0, by convention the variable is not considered a useful predictor variable. When the odds ratio is 1, then a change in value of the independent variable is not associated with a change in the odds that the dependent variable equals a given value. Note in SPSS this is referenced as the confidence interval of Exp(B), where Exp(B) is the odds ratio