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Basic Question 2 of 4
Which of the following statements is (are) true with respect to predicting the value of a dependent variable using a regression? I. As the value of the independent variable increases, so will the value of the dependent variable.
II. Confidence interval around predicted values for the dependant variable will increase as the degrees of freedom increase.
III. The variance of predicted values is simply the square of the standard error of the estimate (SEE).
IV. The farther the independent variable is from its mean, the greater the dispersion will be around the predicted value for the dependent variable.
User Contributed Comments 4
| User | Comment |
|---|---|
| Andrewua | what about second? |
| shival | it is already being answered in the explanation |
| chris76 | I actually disagree with 4. The way the question is worded it seems to imply that a large x value will cause a large error in estimation. But barring conditional heteroskedacity this is simply not the case. I understand the explanation saying if there is more variance in the independent variable but this is a property of a set of numbers and not of an individual number(think if the x values were 1,2,3,4 we would not expect y^(4) to have larger error...). |
| REITboy | Andrewua - more dof --> smaller t-stat --> smaller confidence interval |
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Colin Sampaleanu
Learning Outcome Statements
calculate and interpret a predicted value for the dependent variable, given the estimated regression model and assumed values for the independent variable.
CFA® 2025 Level II Curriculum, Volume 1, Module 2.