Check for Collinearity Among Predictor Variables in Multiple Regression $= 972 - 7.13$

question 13

Multiple Choice

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 Employees is The regression equation is
Employees $= 972 - 7.13$ Innovative Index $+ 1.5$ Job Growth $+ 215$ Industry

$\begin{array} { l r r r r } \text { Predictor } & \text { Coef } & \text { SE Coef } & \text { T } & \text { P } \\ \text { Constant } & 972.4 & 250.7 & 3.88 & 0.001 \\ \text { Innovative Index } & - 7.134 & 6.360 & - 1.12 & 0.277 \\ \text { Job Growth } & 1.54 & 31.45 & 0.05 & 0.961 \\ \text { Industry } & 215.1 & 196.3 & 1.10 & 0.288 \end{array}$

$S = 319.230 \quad R - S q = 8.8 \%$

• A) 8.33
• B) 1.10
• C) 319.23
• D) 1.00
• E) 3.20

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Definitions:

Interval Scale

A scale of measurement where the intervals between values are evenly distributed, allowing for meaningful comparisons of differences.

Frequency Distribution

A statistical tool that illustrates how often each value in a set of data occurs.

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Statistical Terms

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