Help with Question 3 and 4, please. QUESTION 2 2 points Consider the following o

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Help with Question 3 and 4, please.

QUESTION 2 2 points Consider the following output from a regression analysis 0.98223752 0.96479055 Multiple R R Square Adjusted R Square Standard Eror0.23310465 Observations 0.95518798 15 MS 5.459428 100.4721 2.82244E-08 0.054338 Significance F Regressiorn Residual Total 16.37828444 0.597715562 16.976 P-value Intercept X1 X2 X3 Coefficients Standard ErrortStat 3.98076893 1.573044228 0.07321648 0.020889718 0.0323216 0.020895785 0.0038861 0.003833348 2.530615 0.027943 3.504905 0.004928 1.5468 0.150182 1.01376 0.332478 The output shows that: the overall model is significant at an alpha level of 0.05 O 95% of the residuals are within 0.2331 The coefficient of multiple determination for this model is 0.9822 All of the above statements are true QUESTION 3 2 points Save Answer In multiple regression analysis, outliers may be identified by examining the: coefficient of multiple determination. Op-values for the partial coefficients O residuals O correlation coefficient. QUESTION 4 2 points Save Answer An appropriate method to identify multicollinearity in a regression model is to O examine the ANOVA table O examine a correlation matrix examine the R of the regression model. examine a residual plot.

Explanation / Answer

Question 3)

In multiple regression analysis, outliers may be identified by examining the:

Ans: Residuals.

Generally, outliers can be identified as those observations whose standardised residual values are above 3.3 (or less than -3.3).

Also, the scatter plot of residuals can be used to identify the outliers.

Question 4)

An appropriate method to identify multicollinearity in a regression model is to:

Ans: examine a correlation matrix.

Correlation matrix can tell us which dependent variables are correlated to each other.

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