Search
- Filter Results
- Location
- Classification
- Include attachments
- https://stats.libretexts.org/Bookshelves/Computing_and_Modeling/Supplemental_Modules_(Computing_and_Modeling)/Regression_Analysis/Multivariable_RegressionWhen there are many predictors, it is often of interest to see if one or a few of the predictors can do the job of estimation of the mean response and prediction of new observations well enough. This ...When there are many predictors, it is often of interest to see if one or a few of the predictors can do the job of estimation of the mean response and prediction of new observations well enough. This can be put in the framework of comparison between a reduced regression model involving a subset of the variables versus the full regression model involving all the variables.
- https://stats.libretexts.org/Courses/Cerritos_College/Introduction_to_Statistics_with_R/14%3A_Multiple_and_Logistic_Regression/14.02%3A_Model_SelectionThe best model is not always the most complicated. Sometimes including variables that are not evidently important can actually reduce the accuracy of predictions. In this section we discuss model sele...The best model is not always the most complicated. Sometimes including variables that are not evidently important can actually reduce the accuracy of predictions. In this section we discuss model selection strategies, which will help us eliminate from the model variables that are less important. In this section, and in practice, the model that includes all available explanatory variables is often referred to as the full model. Our goal is to assess whether the full model is the best model.
- https://stats.libretexts.org/Bookshelves/Introductory_Statistics/OpenIntro_Statistics_(Diez_et_al)./08%3A_Multiple_and_Logistic_Regression/8.02%3A_Model_SelectionThe best model is not always the most complicated. Sometimes including variables that are not evidently important can actually reduce the accuracy of predictions. In this section we discuss model sele...The best model is not always the most complicated. Sometimes including variables that are not evidently important can actually reduce the accuracy of predictions. In this section we discuss model selection strategies, which will help us eliminate from the model variables that are less important. In this section, and in practice, the model that includes all available explanatory variables is often referred to as the full model. Our goal is to assess whether the full model is the best model.
