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.
Predictive Modeling with Logistic Regression using SAS
Predictive Modeling with Logistic Regression using SAS
This course is part of SAS Statistical Business Analyst Professional Certificate
Instructor: Marc Huber
7,495 already enrolled
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Gain insight into a topic and learn the fundamentals.
4.7
(58 reviews)
Intermediate level
Some related experience required
16 hours to complete
3 weeks at 5 hours a week
Flexible schedule
Learn at your own pace
Skills you'll gain
Details to know
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Assessments
40 assignments
Taught in English
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This course is part of the SAS Statistical Business Analyst Professional Certificate
When you enroll in this course, you'll also be enrolled in this Professional Certificate.
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There are 7 modules in this course
What's included
1 video6 readings
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.
What's included
15 videos1 reading6 assignments
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.
What's included
18 videos1 reading4 assignments
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.
What's included
26 videos9 assignments
In this module, you learn how to select the most predictive variables to use in your model.
What's included
23 videos1 reading12 assignments
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.
What's included
30 videos1 reading9 assignments
What's included
1 reading1 app item
Instructor
Instructor ratings
4.6 (16 ratings)Offered by
Recommended if you're interested in Data Analysis
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