Use Indicator (Dummy) Variables in Multiple Regression $$S = 0.795553 \quad R - S q = 94.3 \% \quad R - S q ( \operatorname { adj } ) = 93.7 \%$$

Use indicator (dummy) 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 a number of variables in
An attempt to develop a model to predict Turnover Rate (%) . The final model deemed
Most appropriate includes two predictor variables: Job Growth (%) and Industry. The
Results are shown below. The predicted turnover rate for a firm in the financial services
Sector with a 2% job growth rate is Dependent Variable is Turnover Rate
$\begin{array} { l r r r r } \text { Predictor } & \text { Coef } & \text { SE Coef } & \text { T } & \text { P } \\ \text { Constant } & 8.8384 & 0.2776 & 31.83 & 0.000 \\ \text { Job Growth } & - 0.57358 & 0.06686 & - 8.58 & 0.000 \\ \text { Industry } & - 3.1395 & 0.4236 & - 7.41 & 0.000 \end{array}$
$$S = 0.795553 \quad R - S q = 94.3 \% \quad R - S q ( \operatorname { adj } ) = 93.7 \%$$

A) 8.25%
B) 7.69%
C) 4.56%
D) 6.19%
E) None of the above.