Session 11: Methods - Selecting Categorical and Quantitative Variables in Linear Regression Analysis
Presentation Type
Event
Abstract
Variable selection is an important means to construct a model that predicts a target/responsible variable with a set of predictable variables. The predictable variables could include quantitative, binary, and/or categorical variables; however, commonly used variable selection methods such as forward selection, backward selection, and stepwise selection are more focused on quantitative variables. It will be a helpful addition to multiple linear regression if categorical variables can be integrated with the commonly used variable selection methods. We proposed a generalized variable selection method that can be used to select both categorical and quantitative variables simultaneously. The detailed results will be presented at the symposium.
Start Date
2-12-2018 3:30 PM
End Date
2-12-2018 5:00 PM
Session 11: Methods - Selecting Categorical and Quantitative Variables in Linear Regression Analysis
University Student Union: Dakota Room 250 A/C
Variable selection is an important means to construct a model that predicts a target/responsible variable with a set of predictable variables. The predictable variables could include quantitative, binary, and/or categorical variables; however, commonly used variable selection methods such as forward selection, backward selection, and stepwise selection are more focused on quantitative variables. It will be a helpful addition to multiple linear regression if categorical variables can be integrated with the commonly used variable selection methods. We proposed a generalized variable selection method that can be used to select both categorical and quantitative variables simultaneously. The detailed results will be presented at the symposium.