Most computer solutions for multiple regression begin with a correlation matrix. Examining this matrix s often the first step when analyzing a regression problem that involves more than one independent variable. Answer the following questions concerning the correlation matrix given in the table below. 1 2 3 4 5 6 1 1.00 0.55 0.20 -0.51 0.79 0.70 2 1.00 0.27 0.09 0.39 0.45 3 1.00 0.04 0.17 0.21 4 1.00 -0.44 -0.14 5 1.00 0.69 6 1.00 A. If variable 1 is the dependent variable, which independent variables have the highest degree of linear association with variable 1? B. What kind of association exists between variables 1 and 4? C. Does this correlation matrix show any evidence of multicollinearity? D. In your opinion, which variable or variables will be included in the best forecasting model? Please give an explanation. E. why are all the entries on the main diagonal equal to 1.00? F. Why is the bottom half of the matrix below the main diagonal blank