Which of the following procedures in model selections is an attempt to find the best regression model without examining all possible models?
A)principle of parsimony
B)cross validation
C)stepwise regression
D)collecting new data and using regression to validate the model

Question

Quizplus · Accepted Answer

Stepwise regression is a procedure used in model selection to iteratively add or remove independent variables from a multiple regression model until a satisfactory subset of predictors is achieved. This approach is not guaranteed to find the best model, but it is an efficient and practical way to search for the most parsimonious model without examining all possible models. The principle of parsimony (A) refers to the idea of choosing the simplest model that fits the data well. Cross-validation (B) is a technique for evaluating the performance of a model on a separate dataset, but it does not necessarily help in selecting the best model. Collecting new data and using regression to validate the model (D) is a good practice, but it does not directly address the issue of model selection. Stepwise regression is a method used to automatically select the best subset of predictors. It evaluates all possible models and selects the best one based on a specific criterion (e.g. AIC or BIC). Therefore, it is an attempt to find the best regression model without examining all possible models. Options A, B, and D do not eliminate the need to examine all possible models.

Which of the Following Procedures in Model Selections Is an Attempt