Inspection of the following table of correlation coefficients for variables in a
ID: 3046393 • Letter: I
Question
Inspection of the following table of correlation coefficients for variables in a multiple regression analysis reveals potential multicollinearity with variables:
y
x1
x2
x3
x4
x5
y
1
x1
0.854168
1
x2
-0.11828
-0.00383
1
x3
-0.12003
-0.08499
-0.14523
1
x4
0.525901
0.118169
-0.14876
0.050042
1
x5
-0.18105
-0.07371
0.995886
-0.14151
-0.16934
1
X1 and X4
X3 and X4
X2 and X5
X2 and X4
y
x1
x2
x3
x4
x5
y
1
x1
0.854168
1
x2
-0.11828
-0.00383
1
x3
-0.12003
-0.08499
-0.14523
1
x4
0.525901
0.118169
-0.14876
0.050042
1
x5
-0.18105
-0.07371
0.995886
-0.14151
-0.16934
1
Explanation / Answer
y
x1
x2
x3
x4
x5
y
1
x1
0.854168
1
x2
-0.11828
-0.00383
1
x3
-0.12003
-0.08499
-0.14523
1
x4
0.525901
0.118169
-0.14876
0.050042
1
x5
-0.18105
-0.07371
0.995886
-0.14151
-0.16934
1
y
x1
x2
x3
x4
x5
y
1
x1
0.854168
1
x2
-0.11828
-0.00383
1
x3
-0.12003
-0.08499
-0.14523
1
x4
0.525901
0.118169
-0.14876
0.050042
1
x5
-0.18105
-0.07371
0.995886
-0.14151
-0.16934
1
Multicollinearity means strong correlation between two independent variables.
From above table
The correlation between X2 and X5 is 0.9959
Hence there is a strong multicollinearity between X2 and X5
y
x1
x2
x3
x4
x5
y
1
x1
0.854168
1
x2
-0.11828
-0.00383
1
x3
-0.12003
-0.08499
-0.14523
1
x4
0.525901
0.118169
-0.14876
0.050042
1
x5
-0.18105
-0.07371
0.995886
-0.14151
-0.16934
1