Why is the R2 is not a very good way to judge, if the specification of...
Why is the R2 is not a very good way to judge, if the specification of a multiple regression model has been improved by adding another variable?
Homework Answers
On adding a predictor to a model, the R-squared increases, even
if due to chance alone. It never decreases. Consequently, a model
with more terms may appear to have a better fit simply because it
has more terms.If a model has too many predictors and higher order
polynomials, it begins to model the random noise in the data. This
condition is known as overfitting the model and it produces
misleadingly high R-squared values and a lessened ability to make
predictions
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Why is the R2 is not a very good way to judge, if the
specification of...
Two linear regression models are fitted using software and below is their R2 and adjusted R2...
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QUESTION 3 Which of the following statements describes why multiple regression is often a superior statistical technique over bivariate regression? If done properly, in multiple regression analysis, you get better estimates of the coefficients than you would in a bivariate regression analysis. If done properly, in multiple regression analysis, you get better estimates of the dependent variable than you would in a bivariate regression analysis. If done properly, in multiple regression analysis, you get an improved R-square and adjusted R-...
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