A study considered a sample of 50 observations used to predict SALES. Included in the analysis were 9 predictors variables, ( Independent Variables).
Correlations
X 1 
X 2 
X 3 
X 4 
X 5 
X 6 
X 7 
X 8 
X 9 

X 2 
0.804 

X 3 
0.625 
0.443 

X 4 
0.032 
0.032 
0.231 

X 5 
0.159 
0.214 
0.177 
0.194 

X 6 
0.319 
0.373 
0.308 
0.054 
0.293 

X 7 
0.016 
0.030 
0.079 
0.168 
0.309 
0.067 

X 8 
0.026 
0.103 
0.015 
0.151 
0.311 
0.059 
0.912 

X 9 
0.169 
0.027 
0.104 
0.017 
0.248 
0.114 
0.174 
0.223 

SALES 
0.764 
0.630 
0.756 
0.149 
0.171 
0.426 
0.145 
0.141 
0.068 
1)yes data having multicollinearity,
if independent dependent to each other then we can say that data have multicollinearity ,,
2)now we observ above correlation matrix, pairs are:
(x1,x2)
(x1,x3)
this pair included in multicollinearity.
3)0.50 correlation limit we can check
4)Using Principal componunt analysis (PCA) we reduce multicollinearity
5) x9 is very less relation with sales ,correlation between them is 0.068
we assume less then 0.20 correlation value, we can say weak relation,
so x4, x5, x7, x8 also showing weak correlation.
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