Title

Session 11: Methods - Selecting Categorical and Quantitative Variables in Linear Regression Analysis

Presenter Information/ Coauthors Information

Jixiang Wu, South Dakota State University

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

12-2-2018 3:30 PM

End Date

12-2-2018 5:00 PM

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Feb 12th, 3:30 PM Feb 12th, 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.