Kıbrıs Predictive Modeling Using Logistic Regression Eğitimi

Before attending this course, you should:

• Have experience executing SAS programs and creating SAS data sets, which you can gain from the SAS Programming 2: Data Manipulation Techniques course.
• Have experience building statistical models using SAS software.
• Have completed a statistics course that covers linear regression and logistic regression, such as the Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression course..

This course addresses SAS/SHARE software.

Learn how to:

• Use logistic regression to model an individual's behavior as a function of known inputs.
• Create effect plots and odds ratio plots using ODS Statistical Graphics.
• Handle missing data values.
• Tackle multicollinearity in your predictors.
• Assess model performance and compare models.

Predictive Modeling

• Business applications.
• Analytical challenges.

Fitting the Model

• Parameter estimation.
• Adjustments for oversampling.

Preparing the Input Variables

• Missing values.
• Categorical inputs.
• Variable clustering.
• Variable screening.
• Subset selection.

Classifier Performance

• ROC curves and lift charts.
• Optimal cutoffs.
• K-S statistic.
• c statistic.
• Profit.
• Evaluating a series of models.