线性回归模型在SAS_EM中的应用实例

Chapter 3

Predictive Modeling Using Regression

3.1 Introduction to Regression ....................................................................... 3-3 3.2 Regression in Enterprise Miner ............................................................... 3-10

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3.1 Introduction to Regression

Objectives???Describe linear and logistic regression.Explore data issues associated with regression.Discuss variable selection methods.2.专业资料.

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Linear versus Logistic RegressionLinear RegressionTarget is an interval variable.Logistic RegressionTarget is a discrete (binary or ordinal) variable.Input variables have any Input variables have any measurement level.measurement level.Predicted values are the mean of the target variable at the given values of the input variables.Predicted values are the probability of a particular level(s) of the target variable at the given values of the input variables.3 The Regression node in Enterprise Miner does either linear or logistic regression depending upon the measurement level of the target variable. Linear regression is done if the target variable is an interval variable. In linear regression the model predicts the mean of the target variable at the given values of the input variables.

Logistic regression is done if the target variable is a discrete variable. In logistic regression the model predicts the probability of a particular level(s) of the target variable at the given values of the input variables. Because the predictions are probabilities, which are bounded by 0 and 1 and are not linear in this space, the probabilities must be transformed in order to be adequately modeled. The most common transformation for a binary target is the logit transformation. Probit and complementary log-log transformations are also available in the regression node.