Logistic Regression Notes Output Created 29-JUL-2025 15:33:51 Comments Input Data C:\Users\eskra\Downloads\MODULDATA5 (1).sav Active Dataset DataSet1 Filter Weight SAM_WEIGHT Split File N of Rows in Working Data File 400 Missing Value Handling create lengthy paragraph: Interpretation of Weighting Effects The inclusion of the SAM_WEIGHT variable produced several notable effects: Increased Model Generalizability: The weighted model better represents population-level relationships by correcting for oversampled/undersampled groups. AGE Became Statistically Significant: The unweighted model failed to capture the continuous effect of age, possibly due to sample imbalance. Weighting clarified the consistent upward trend in HIGH_BMI probability as age increases. Slight Improvement in Classification Accuracy: Model accuracy improved by 1.5%, especially in correctly predicting LOW_BMI cases (from 59% to 61.5%). Stability of GENHLTH Predictors: The health status variable remained a robust predictor across both models. The odds ratios for GENHLTH(3) and GENHLTH(2) were consistent, suggesting a strong independent effect. Public Health Implications These findings underscore the predictive strength of self-perceived health and age in identifying adults at risk of obesity. The robustness of GENHLTH(3) suggests that "good" health perceptions may mask high BMI risk, highlighting the need for objective health screening even in moderately self-rated healthy individuals