Please evaluate the correlation matrix below, assuming stock price is the respon
ID: 3176636 • Letter: P
Question
Please evaluate the correlation matrix below, assuming stock price is the response variable. If you are using this as a diagnostic tool, what are you looking for? If this was your data, how would you proceed?
Stock Price
Competitiveness
Innovativeness
Firm Size
Firm Age
Stock Price
1.0
Competitiveness
.73
1.0
Innovativeness
.49
.81
1.0
Firm Size
.17
.12
.03
1.0
Firm Age
.26
-.31
-.49
.62
1.0
Stock Price
Competitiveness
Innovativeness
Firm Size
Firm Age
Stock Price
1.0
Competitiveness
.73
1.0
Innovativeness
.49
.81
1.0
Firm Size
.17
.12
.03
1.0
Firm Age
.26
-.31
-.49
.62
1.0
Explanation / Answer
The correlation matrix is symmetric , so the value in the cell (j,i) would be same as that in the cell (i,j).
This correlation matrix comprising of the response variable stock price serves 2 fold. We can check the linear association of each predictor variable on the response variable.
We find that all predictors have positive association with the stock price variable with competitiveness having the highest association of 0.73.
The basic assumption of linear regression analysis is that each of the covariates should be linearly related to the response variable.
Secondly, we can check the correlation coeff between the predictors, if there is any significant correlation between 2 predictors, we say there is multicollinearity,and we have to remove multicollinearity generally by removing some of the predictors.
Here we find that Innovativeness has high correlation with Competitiveness and Firm age has significant association with the rest of the predictors.
Thus we might consider to remove Innovativeness and Firm Age from our set of predictors to remove multicollinearity.