The portion of dependent variable variance left over after the predictor variables are included in a model intended to represent Y is ____.
A)white noise
B)residual
C)correlation
D)covariance

Question

Quizplus · Accepted Answer

The residual is the portion of the dependent variable variance that is not explained by the predictor variables included in the model, and is therefore left over. It represents the unobserved, random error in the model, which can be analyzed to evaluate the model's fit and validity. White noise refers to random variations with no underlying pattern or structure, while correlation and covariance indicate the degree and direction of association between variables, which can be used to assess relationships among predictors and between predictors and the dependent variable, but do not directly represent the residual error term.

The Portion of Dependent Variable Variance Left Over After the Predictor