(1). In a regression class, a student suggested the following: If extremely infl
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Question
(1). In a regression class, a student suggested the following: If extremely influential outlying cases are detected in a data set, simply discard these cases from the data set. Provide your comments an the validity of this statement.
(2). A student asked: Why is it necessary to perform diagnostic checks of the fit when R2 is large? Provide your comments on the validity of this statement.
(3). Describe several informal procedures that can be helpful in identifying multicollinearity among the predictor (X) variables in a multiple linear regression model.
Explanation / Answer
dear studnt please post the question one at a time :
3) You can assess multicollinearity in regression in the following ways:
* The Variance Inflation Factor (VIF) measures the impact of collinearity among the variables in a regression model. The Variance Inflation Factor (VIF) is 1/Tolerance, it is always greater than or equal to 1.
* Examine the correlations and associations (nominal variables) between independent variables to detect a high level of association. High bivariate correlations are easy to spot by running correlations among your variables. If high bivariate correlations are present, you can delete one of the two variables. However, this may not always be sufficient.
* Regression coefficients will change dramatically according to whether other variables are included or excluded from the model.
* The standard errors of the regression coefficients will be large if multicollinearity is an issue.
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