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 7.2, 7.3, and 7.4.

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 project.
• 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.
• 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 (kann variieren, Details s. Terminliste)
Präsenzkurs:	2.0 Tage
Live Web Class:	14 Stunden	Systemvoraussetzungen
E-Learning:	14 Stunden/180 Tag Lizenz	Systemvoraussetzungen

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 for SAS® 9: Getting Started course.

In diesem Kurs wird mit folgenden Software Modulen gearbeitet / This course addresses SAS Visual Statistics Software

Introduction to SAS Visual Statistics

Cluster Segmentation

• Segmentation concepts.
• Cluster analysis.
• Data management.

Models with Continuous Targets

• Managing projects and models.
• Linear regression models.
• Generalized linear models.

Models with Categorical Targets

• Logistic regression.
• Decision trees.

Model Comparison and Assessment

• Comparing models.
• Scoring.