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Using SAS® to Extend Logistic Regression

Dachao Liu

Open publisher page 1 citations

Abstract

Logistic regression is widely used in analysis of categorical data especially data with variables that have binary responses. It can be used in many fields where discrete responses and a set of explanatory variables coexist. For instance, it can be used in survival analysis which often uses data with variable having values of ‘success ’ and ‘failure’. We can let logistic regression do more things for us by extending it. This paper will discuss under what circumstances we extend logistic regression by using SAS.

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What this paper is about

Logistic regression is widely used in analysis of categorical data especially data with variables that have binary responses. It can be used in many fields where discrete responses and a set of explanatory variables coexist. For instance, it can be used in survival analysis which often uses data with variable having values of ‘success ’ and ‘failure’. We can let logistic regression do more things for us by extending it. This paper will discuss under what circumstances we extend logistic regression by using SAS.

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Available abstract

Logistic regression is widely used in analysis of categorical data especially data with variables that have binary responses. It can be used in many fields where discrete responses and a set of explanatory variables coexist. For instance, it can be used in survival analysis which often uses data with variable having values of ‘success ’ and ‘failure’. We can let logistic regression do more things for us by extending it. This paper will discuss under what circumstances we extend logistic regression by using SAS.

Key concepts: Logistic regression, Categorical variable, Logistic model tree, Regression diagnostic, Statistics, Cross-sectional regression, Multinomial logistic regression, Regression analysis

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