This course introduces SAS Visual Statistics for building predictive models in an interactive, exploratory way. Exploratory model fitting is a critical step in modeling big data. This course is appropriate for users of SAS Visual Analytics in SAS Viya 8.3.

The self-study e-learning includes:

• Annotatable course notes in PDF format.
• Virtual Lab time to practice.

Lernen Sie, wie Sie / Learn how to use SAS Visual Statistics to

• Perform statistical analysis of data of any size.
• Create a report with pages.
• Determine useful preferences and settings.
• Create segments, or clusters, of input variables.
• Perform regression and logistic regression modeling.
• Perform decision tree modeling.
• Perform stratified model fitting.
• Perform model validation.
• Compare models.
• Generate score code.

Zielgruppe / Who should attend

Predictive modelers, business analysts, and data scientists who want to take advantage of SAS Visual Statistics for highly interactive, rapid model fitting

Verfügbare Schulungsformen / Formats available	Dauer
Präsenzkurs:	2.0 Tage
Live Web Class:	14 Stunden

Before attending this course, you should have an understanding of regression and logistic regression analysis for predictive modeling. You can gain this knowledge from the Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression course. You should also have experience using SAS Visual Analytics, which you can gain from the SAS® Visual Analytics 1 for SAS® Viya®: Basics course.

In diesem Kurs wird mit folgenden Software Modulen gearbeitet: SAS Visual Statistics, SAS Viya Software

Introduction to SAS Visual Statistics

• Managing reports and pages.
• SAS Viya architecture.

Cluster Segmentation

• Segmentation concepts.
• Cluster analysis.

Models with Continuous Targets

• Linear regression models.
• Generalized linear models.
• Generalized additive models.
• Model validation.

Models with Categorical Targets

• Logistic regression.
• Nonparametric logistic regression.
• Interactive group by.
• Decision trees.

Model Comparison and Assessment

• Comparing models.
• Scoring.