Chartered Financial Analyst (CFA) Practice Exam Level 2

In the context of regression, what does SE(b1) represent?

1. Standard Error of the regression

2. Standard Error of the coefficient estimate

3. Standard Error of the predicted value

4. Standard Error of the model

The correct answer is: Standard Error of the coefficient estimate

The correct answer is the Standard Error of the coefficient estimate. In regression analysis, SE(b1) specifically denotes the standard error associated with the estimated coefficient for the independent variable in the model. This measure reflects the precision of the estimated coefficient, indicating how much the estimated value of the coefficient could vary if the model were to be fitted to different samples from the same population. A smaller standard error implies that the coefficient estimate is more precise and confident, while a larger standard error suggests more variability and uncertainty in the estimate. The significance of this measurement comes into play when conducting hypothesis tests to determine whether the independent variable significantly contributes to explaining variations in the dependent variable. The other options refer to different concepts. The standard error of the regression represents the overall estimation error in predicting the dependent variable, not specific to an individual coefficient. The standard error of the predicted value pertains to the accuracy of individual predictions, which is separate from assessing the coefficient's reliability. Lastly, the standard error of the model typically refers to an assessment that combines multiple coefficients rather than focusing on an individual coefficient's precision.