Exam 15: Multiple Regression

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True or False: So that we can fit curves as well as lines by regression, we often use mathematical manipulations for converting one variable into a different form.These manipulations are called dummy variables.

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Which of the following will NOT change a nonlinear model into a linear model?

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SCENARIO 15-6 SCENARIO 15-6   -True or False: Referring to Scenario 15-6, the variable    should be dropped to remove collinearity? -True or False: Referring to Scenario 15-6, the variable SCENARIO 15-6   -True or False: Referring to Scenario 15-6, the variable    should be dropped to remove collinearity? should be dropped to remove collinearity?

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True or False: In stepwise regression, an independent variable is not allowed to be removed from the model once it has entered into the model.

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SCENARIO 15-6 SCENARIO 15-6   -True or False: Referring to Scenario 15-6, the variable    should be dropped to remove collinearity? -True or False: Referring to Scenario 15-6, the variable SCENARIO 15-6   -True or False: Referring to Scenario 15-6, the variable    should be dropped to remove collinearity? should be dropped to remove collinearity?

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Which of the following is used to find a "best" model?

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SCENARIO 15-4 SCENARIO 15-4    15-16 Multiple Regression Model Building   -Referring to Scenario 15-4, what are, respectively, the values of the variance inflationary factor of the 3 predictors? 15-16 Multiple Regression Model Building SCENARIO 15-4    15-16 Multiple Regression Model Building   -Referring to Scenario 15-4, what are, respectively, the values of the variance inflationary factor of the 3 predictors? -Referring to Scenario 15-4, what are, respectively, the values of the variance inflationary factor of the 3 predictors?

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SCENARIO 15-1 SCENARIO 15-1   - -SCENARIO 15-1   -

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True or False: Collinearity is present if the dependent variable is linearly related to one of the explanatory variables.

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SCENARIO 15-4 SCENARIO 15-4    15-16 Multiple Regression Model Building   -True or False: Referring to Scenario 15-4, there is reason to suspect collinearity between some pairs of predictors. 15-16 Multiple Regression Model Building SCENARIO 15-4    15-16 Multiple Regression Model Building   -True or False: Referring to Scenario 15-4, there is reason to suspect collinearity between some pairs of predictors. -True or False: Referring to Scenario 15-4, there is reason to suspect collinearity between some pairs of predictors.

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A microeconomist wants to determine how corporate sales are influenced by capital and wage Spending by companies.She proceeds to randomly select 26 large corporations and record Information in millions of dollars.A statistical analyst discovers that capital spending by Corporations has a significant inverse relationship with wage spending.What should the Microeconomist who developed this multiple regression model be particularly concerned with?

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SCENARIO 15-4 SCENARIO 15-4    15-16 Multiple Regression Model Building   - 15-16 Multiple Regression Model Building SCENARIO 15-4    15-16 Multiple Regression Model Building   - -SCENARIO 15-4    15-16 Multiple Regression Model Building   -

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In multiple regression, the __________ procedure permits variables to enter and leave the model At different stages of its development.

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SCENARIO 15-2 SCENARIO 15-2   -Referring to Scenario 15-2, given a quadratic relationship between sale price (Y)and property Size (X1), what test should be used to test whether the curves differ from cove and non-cove Properties? -Referring to Scenario 15-2, given a quadratic relationship between sale price (Y)and property Size (X1), what test should be used to test whether the curves differ from cove and non-cove Properties?

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SCENARIO 15-4 SCENARIO 15-4    15-16 Multiple Regression Model Building   - 15-16 Multiple Regression Model Building SCENARIO 15-4    15-16 Multiple Regression Model Building   - -SCENARIO 15-4    15-16 Multiple Regression Model Building   -

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True or False: The stepwise regression approach takes into consideration all possible models.

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True or False: The parameter estimates are biased when collinearity is present in a multiple regression equation.

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