Final answer:
Logistic regression model coefficients (βs) indicate the constant term (β0) and the slopes (β1, β2, etc.) for each predictor variable, representing changes in the log-odds of the outcome for unit changes in the predictors.
Step-by-step explanation:
The coefficients in a logistic regression model are represented by the βs, where β0 is the constant term (β0 or the y-intercept), β1 is the coefficient for the first predictor (x1), β2 is the coefficient for the second predictor (x2), and so on for additional predictors. These coefficients are analogous to the slope terms in linear regression but interpreted differently as they represent the log-odds of the outcome variable.
β0 (or constant term): This is the value of the output when all the predictor variables are equal to zero. In the context of a logistic regression model, β0 is the log-odds of the dependent variable when all the independent variables are at their reference levels.
β1 (coeff. for x1): This represents the change in the log-odds of the dependent variable for a one-unit increase in x1, holding all other variables constant.
β2 (coeff. for x2): Similarly, this shows the change in the log-odds of the dependent variable for a one-unit increase in x2, while other variables are held constant.