22.) Using the parameter estimates in the JMP output, is there a problem with multicollinearity? YES NO CANNOT ASSESSJustification: What other method(s) may be used to assess multicollinearity? 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

The Multicollinearity occurs when two or more independent variables are highly correlated with one another in a regression model. The VIF score of an independent variable represents how well the variable is explained by other independent variables. VIF starts at 1 and has no upper limit and is given as: VIF = 11 - R2 , R2 : Coefficient of determination In practice, when VIF = 1, there is no correlation between the independent variable and the other variables but when VIF exceeding the value 5 or 10 indicates high multicollinearity between this independent variable and the others. The parameter estimates in the JMP output which has the problem with multicollinearity is identified as : Parameter Estimates Term Estimate Std Error t Ratio Prob > |t| VIF x3 -1.37482 1.720724 -0.8 0.4368 15.60094 x4 1.341993 0.789778 1.7 0.1099 13.06796 where, VIF > 5Therefore, YES, when Using the parameter estimates in the JMP output, if there is any problem with multicollinearity.