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Secure https://utsa.blackboard.com/webapps/assessment/take/launch.jsp?course assessment i Question Completion Status: Consider the following output from a regression analysis 0.98223752 0.96479055 Multiple R R Square Adjusted R 0.95518798 Square Standard Error 0.23310465 Observations 1 df MS Significance F Regression Residual Total 16.37828444 5.459428 100.4721 2.82244E-08 0.S97715562 0.054338 16.976 14 X1 X2 x3 Coefficients Standard Error tStatP-value 3.98076893 1573044228 2.53061s 0.027943 0.07321648 0.020889718 3.50490s 0.004928 0.0323216 0.020895785 -1.5468 0.150182 0.0038861 0.0038333481.01376 0.332478 Multicollincarity likely cxists between variables: XI and X3 X2 and X3 X2 and XI It cannot be determined from the information given. Click Save and Submit to sqve and submit. Click Save All Answers to sqve all answers N test 2 review pages Daft OT11e.TB Ch07.doe

Explanation / Answer

1)

cant be determined

VIF determined the presence of co-linearity in the data. VIF is not given here. VIF>3 indicates multicolinearity

2)

4

# dummy variables = total number of categories - 1 = 5-1= 4

3)

higher order terms

To use curvilinear regression analysis, we test several polynomial regression equations. These eqns are linear, quadratic or cubic.

4)

sample size is 15

5)

indicator variable

as it has only 2 categories either "0" or "1"

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