In multiple regression analysis, residual analysis is used to test the requirement that the number of independent variables included in the analysis is correct the variation in the residuals is the same for all predicted values of y the independent variables are the direct cause of the dependent variable the prediction error is minimized

The predicted error is minimized.

Step-by-step explanation:

Ideally, residual analysis is used in a linear regression model to measure the appropriateness of the model by examining the residual plots on the graph.

And, residual referred as a difference between the noticed value of the dependent variable (y) and the estimated value (ŷ).  

Residual = Noticed value - Estimated value  

e = y - ŷ

step-by-step explanation:

squares, rhombus, rectangles