In a multiple regression analysis, if the model provides a poor fit, this indicates that:
A) the sum of squares for error will be large.
B) the standard error of estimate will be large.
C) the multiple coefficient of determination will be close to zero.
D) All of these choices are correct.

Question

Quizplus · Accepted Answer

If the model provides a poor fit, it means that the actual values of the dependent variable differ significantly from the predicted values by the model. This indicates that the sum of squares for error will be large, as there will be large differences between the predicted and actual values. Additionally, the standard error of estimate will be large, as the model's predicted values will be highly variable and thus have a higher margin of error. Finally, the multiple coefficient of determination (R-squared) measures how well the independent variables explain the variation in the dependent variable. If the model provides a poor fit, the R-squared value will be close to zero, indicating that the independent variables are not explaining much of the variation in the dependent variable. Therefore, all of these options are correct.

In a Multiple Regression Analysis, If the Model Provides a Poor