Check for Collinearity Among Predictor Variables in Multiple Regression $=40.9+3.99$

Check for collinearity among predictor variables in multiple regression.
-A sample of 22 firms was selected from the high tech industry (Industry = 1) and the
Financial services sector (Industry = 0) . Data were collected on the following variables:
Turnover rate, job growth, number of employees, and innovative index (higher scores
Indicate a more innovative and creative organizational culture) . A multiple regression
Model is developed to predict Turnover Rate. However, to check for the possibility of
Collinearity, a regression among just the predictor variables was run. Based on the results
Shown below, the Variance Inflation Factor (VIF) for the predictor variable Innovative
Index is The regression equation is
Innovative Index $=40.9+3.99$ Job Growth -0.00612 Employees

$\begin{array}{lrrrr}\text { Predictor } & \text { Coef } & \text { SE Coef } & \text { T } & \text { P } \\ \text { Constant } & 40.932 & 8.162 & 5.01 & 0.000 \\ \text { Job Growth } & 3.9863 & 0.8912 & 4.47 & 0.000 \\ \text { Employees } & -0.006123 & 0.009296 & -0.66 & 0.518\end{array}$

$S=13.1511 \quad R-S q=52.5 \%$

A) 52.5
B) 13.1511
C) 3.63
D) 2.10
E) 1.00