This course covers predictive modeling using SAS/STAT software with emphasis on the LOGISTIC procedure. This course also discusses selecting variables and interactions, recoding categorical variables based on the smooth weight of evidence, assessing models, treating missing values, and using efficiency techniques for massive data sets. You learn to use logistic regression to model an individual's behavior as a function of known inputs, create effect plots and odds ratio plots, handle missing data values, and tackle multicollinearity in your predictors. You also learn to assess model performance and compare models.
This course is part of the SAS Statistical Business Analyst Professional Certificate
About this Course
Skills you will gain
- Logistic Regression
- Predictive Modelling
Syllabus - What you will learn from this course
Course Overview and Logistics
Understanding Predictive Modeling
Fitting the Model
Preparing the Input Variables, Part 1
Preparing the Input Variables, Part 2
- 5 stars80.95%
- 4 stars9.52%
- 2 stars4.76%
- 1 star4.76%
TOP REVIEWS FROM PREDICTIVE MODELING WITH LOGISTIC REGRESSION USING SAS
Very completed and deep knowledge shared with very friendly ways, explained the knowledge very clearly. Also the practices help me to understand the knowledge better.
Great training sets of problems. Good guidance & teaching.
Thank you so much to the instructor, Michael J Patetta for teaching this course!
This was another great course from SAS and Coursera. I had no experience with predictive modelling prior to the course and learned quite a bit about modelling in the SAS environment.
About the SAS Statistical Business Analyst Professional Certificate
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