Which model is the “best” at predicting response, y?Justification: JMP OUTPUT- #21-24ModelNumber RSquareRMSECpx210.9566 19.16692.0888x310.7637 44.751182.6080This highlightx110.0257 90.8632390.5179does not meanx2,x30.9604 18.86122.5432anything. It isjust a quirk ofх1,х220.9578 19.46613.6213х1,x320.7637 46.043984.5881JMP.х1,x2,x330.9617 19.11984.0000Figure 1: Table of Possible Predictive ModelsParameter EstimatesTermEstimate Std Error t Ratio Prob> |t|VIFIntercept 9.600283738.541010.250.8067х1-1.2168781.188857-1.020.32231.0660932x25.39780720.562969.59<.0001* 3.9067774x3-1.374817 1.720724-0.800.436815.600942x41.34199330.7897781.700.109913.067958Figure 2: Parameter Estimates for x1, x2, x3, and x46-6-Positive44-4.-4-NegativeNegative-6--6--830.032.535.0-830.032.535.0Figure 3: Studentized Residual Plots A (Left) and B (Right)SLR Residual in YQR Residual in Y

Root Mean Square Error (RMSE) is the standard deviation of the residuals (prediction errors) which is a measure of how far from the regression line data points are. RMSE is a measure of how scattered out these residuals are that is it tells us how concentrated the data is around the line of best fit. Also, the general consensus about Mallow's CP is that smaller the Cp values are the better as they indicate smaller amounts of unexplained error. Here, the best model at predicting response, y is given as: Model Number RSquare RMSE CP x2, x3 2 0.9604 18.8612 2.5432 where, RMSE and CP is least for the chosen model in comparison to other models.

Answered: Which model is the “best” at predicting…

Math

Statistics

Which model is the “best” at predicting response, y? Justification:

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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