Multicollinerity among individual variable coefficients results in ___________?
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Answer: C. increase in standard error
Explanation
Multicollinearity among independent variable coefficients results in an increase in the standard error. It occurs when predictor variables in a regression model are highly correlated with each other. This high correlation makes it difficult for analysts to isolate the individual impact of each variable. Multicollinearity does not reduce overall model predictive power but distorts individual coefficient tests. Standard error measures the statistical precision and uncertainty associated with regression coefficients. Analysts test for multicollinearity using variance inflation factor metrics during quantitative modeling.
Cost analysts must check for multicollinearity when building regression models with multiple economic drivers. Removing redundant independent variables helps resolve multicollinearity issues in forecasting models. Accurate regression coefficients are vital for reliable cost estimation and managerial decision-making. Decrease in variance is incorrect because multicollinearity inflates rather than reduces coefficient variance. Increase in variance error is an imprecise phrase compared to standard error terminology. Decrease in standard error contradicts statistical theory since multicollinearity inflates standard errors.
- Subject
- Accounting and Finance
- Category
- Accounting
- Topic
- —
- Difficulty
- medium
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