Predictive Modeling with Logistic Regression using SAS

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  • Course Overview and Logistics
  • Understanding Predictive Modeling
    • In this module, you review the fundamentals of predictive modeling. Then you explore the business scenario data that is used throughout the course. Finally, you learn about common analytical challenges that you might encounter as a modeler.
  • Fitting the Model
    • In this module, you investigate the concepts behind the logistic regression model. Then you learn to use the LOGISTIC procedure to fit a logistic regression model. Finally, you learn how to score new cases and adjust the model for oversampling.
  • Preparing the Input Variables, Part 1
    • In this module, you learn how to deal with common problems with your predictor variables such as missing values, categorical predictors with many levels, a high number of redundant predictors, and nonlinear relationships with the response variable.
  • Preparing the Input Variables, Part 2
    • In this module, you learn how to select the most predictive variables to use in your model.
  • Measuring Model Performance
    • In this module, you learn how to assess the performance of your model and how to determine allocation rules that maximize profit. Finally, you learn how to generate a family of increasingly complex predictive models and how to select the best model.
  • SAS Certification Practice Exam - Statistical Business Analysis Using SAS®9: Regression and Modeling