Financial Risk Manager Part 1
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Which of the following best explains why multiple linear regression analysis may be preferred to single linear regression?
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NNikitesh
A
It is simpler to model using modern software and computer programming
B
It reduces the omitted variable bias
C
It's easier to model and establishes the relationship between the dependent variable and important independent variables
D
None of the above
Explanation:
Explanation:
Multiple linear regression analysis is preferred over single linear regression primarily because it helps reduce omitted variable bias.
Key points:
- Omitted variable bias occurs when a regression model excludes relevant independent variables that affect the dependent variable.
- When omitted variables are correlated with included independent variables, it can lead to biased coefficient estimates.
- Multiple regression allows inclusion of multiple explanatory variables, providing a more complete model specification.
- While modern software makes both simple and multiple regression accessible, the primary statistical advantage of multiple regression is addressing omitted variable bias.
- Option A is incorrect because multiple regression is not necessarily simpler than single regression.
- Option C is incorrect because multiple regression is generally more complex to model than single regression.
Why B is correct: Multiple regression reduces omitted variable bias by allowing the inclusion of more relevant explanatory variables, leading to more accurate coefficient estimates and better model specification.
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