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?
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?
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question
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?

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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