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This analysis explores how to model a health outcome with more than two categories using data from 5,000 U.S. adults in the 2020 BRFSS survey. The outcome is self-reported general health grouped into three levels: Excellent/Very Good, Good, and Fair/Poor. Predictors include age, sex, BMI, physical activity, income, and smoking status. Two modeling approaches are compared: multinomial logistic regression, which treats the outcome categories as unordered, and ordinal logistic regression, which takes advantage of the natural health ranking. The analysis also checks whether the proportional odds assumption holds and uses AIC to determine which model fits the data better.

SIM_Tugas_B_M0725101

Tugas Sistem Informasi Manajemen Annisa Cahyaningtyas - M0724048

Online Shopper Analysis - Data Cleaning and EDA