Multinomial Logistic Regression is a statistical analysis method used when the outcome variable is categorical with more than two levels. Unlike binary logistic regression, which deals with binary outcomes, multinomial logistic regression is applied when the dependent variable has three or more unordered categories, such as predicting the type of fruit (e.g., apple, orange, banana) based on certain characteristics. This method calculates the probabilities of different possible outcomes of a dependent variable categorized by a set of independent variables. These independent variables can be nominal, ordinal, interval, or ratio-level. Multinomial logistic regression extends the concept of logistic regression by splitting the outcome into multiple levels. It provides a set of coefficients for each level, which helps understand how the predictor variables affect the probability of each category. This method is used for predictive analysis in social sciences, medical, and market research.