Final answer:
In a multinomial logistic regression model, the dependent variable is the categorical outcome of interest, and the independent variables are the predictors. The y-intercept is the expected log odds of the dependent variable when all predictors are zero, and the slope represents the change in log odds per one unit increase in the predictor. Odds ratios above 1 indicate a positive association, while those below 1 indicate a negative association.
Step-by-step explanation:
In the context of a multinomial logistic regression model, the dependent variable is the outcome that we are trying to predict or explain, and it is typically categorical with more than two categories. The independent variables are the predictors or factors that are presumed to influence the dependent variable.
The y-intercept is the value of the dependent variable when all independent variables are equal to zero. The slope coefficients in logistic regression represent the change in the log odds of the dependent variable for a one unit change in the independent variable.
When interpreting the odds ratio (OR) or relative risk (RR), a value greater than 1 indicates a positive association between the independent variable and the outcome, while a value less than 1 indicates a negative association. For instance, an OR of 1.5 for a variable would mean that for each one-unit increase in that variable, the odds of the outcome occurring are 1.5 times greater, assuming all other variables are held constant.