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

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22.) Using the parameter estimates in the JMP output, is there a problem with multicollinearity?

                  YES                  NO                CANNOT ASSESS

Justification:

 

What other method(s) may be used to assess multicollinearity?

JMP OUTPUT- #21-24
Model
Number RSquare
RMSE
Cp
x2
1
0.9566 19.1669
2.0888
x3
1
0.7637 44.7511
82.6080
This highlight
x1
1
0.0257 90.8632
390.5179
does not mean
x2,x3
0.9604 18.8612
2.5432
anything. It is
just a quirk of
х1,х2
2
0.9578 19.4661
3.6213
х1,x3
2
0.7637 46.0439
84.5881
JMP.
х1,x2,x3
3
0.9617 19.1198
4.0000
Figure 1: Table of Possible Predictive Models
Parameter Estimates
Term
Estimate Std Error t Ratio Prob> |t|
VIF
Intercept 9.6002837
38.54101
0.25
0.8067
х1
-1.216878
1.188857
-1.02
0.3223
1.0660932
x2
5.3978072
0.56296
9.59
<.0001* 3.9067774
x3
-1.374817 1.720724
-0.80
0.4368
15.600942
x4
1.3419933
0.789778
1.70
0.1099
13.067958
Figure 2: Parameter Estimates for x1, x2, x3, and x4
6-
6-
Positive
4
4
-4.
-4-
Negative
Negative
-6-
-6-
-8
30.0
32.5
35.0
-8
30.0
32.5
35.0
Figure 3: Studentized Residual Plots A (Left) and B (Right)
SLR Residual in Y
QR Residual in Y
Transcribed Image Text:JMP OUTPUT- #21-24 Model Number RSquare RMSE Cp x2 1 0.9566 19.1669 2.0888 x3 1 0.7637 44.7511 82.6080 This highlight x1 1 0.0257 90.8632 390.5179 does not mean x2,x3 0.9604 18.8612 2.5432 anything. It is just a quirk of х1,х2 2 0.9578 19.4661 3.6213 х1,x3 2 0.7637 46.0439 84.5881 JMP. х1,x2,x3 3 0.9617 19.1198 4.0000 Figure 1: Table of Possible Predictive Models Parameter Estimates Term Estimate Std Error t Ratio Prob> |t| VIF Intercept 9.6002837 38.54101 0.25 0.8067 х1 -1.216878 1.188857 -1.02 0.3223 1.0660932 x2 5.3978072 0.56296 9.59 <.0001* 3.9067774 x3 -1.374817 1.720724 -0.80 0.4368 15.600942 x4 1.3419933 0.789778 1.70 0.1099 13.067958 Figure 2: Parameter Estimates for x1, x2, x3, and x4 6- 6- Positive 4 4 -4. -4- Negative Negative -6- -6- -8 30.0 32.5 35.0 -8 30.0 32.5 35.0 Figure 3: Studentized Residual Plots A (Left) and B (Right) SLR Residual in Y QR Residual in Y
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