Predictor Envelopes and Standard Regression Models: An Empirical Juxtaposition

This study compares predictor envelopes and standard regression models using data on the attributes (Weight, Height, Shoe Size, Chest Diameter, Diastolic Blood Pressure, Fasting Blood Sugar, and Age) of pregnant women obtained from General Hospital, Offa. The purpose is to use both methods to fit models and determine which of the two is more efficient in prediction. The study applied predictor envelopes method as well as standard regression method to the data and was analyzed with the support of Renvlp statistical software. It was found that the information criterion AIC and BIC agreed to u=2 sufficient dimension. It was also discovered that both methods declare variable height as the only active predictor of weight of pregnant women due to their estimates (51.07, 52.03), z-scores (0.44, 0.45), and ratios of asymptotic standard error (0.99) The predictive performance of the two models show that the standard error of predictor envelopes estimates are smaller than those of the standard regression estimates. Thus, Predictor envelope is better than standard regression method even with one response variable y.