Question

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

Answer 1

Answer: 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

Answer 2

Multiple regression analysis is used to make a linear model capable of giving predicting an output variable using two or more independent variables. Analysis of the residual is used to to test if the variation in the residuals is the same for all predicted values of y.

• Residual values gives the difference between the actual and predicted value of a model.

• Residual analysis in linear regression is used to test the appropriateness of a linear model for a given data set.

• Since, the number of independent variables in multiple regression exceeds 1 ; then variation in the predicted values are analysed using the result of the residuals.

Therefore, residual analysis in multiple regression tests the variation in the residuals is the same for all predicted values of y.

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