This course is designed for SAS Enterprise Guide users who want to perform statistical analyses. The course is written for SAS Enterprise Guide 7.1 along with SAS 9.4, but students with previous SAS Enterprise Guide versions will also get value from this course. An e-course is also available for SAS Enterprise Guide 5.1 and SAS Enterprise Guide 4.3.

Learn how to

• generate descriptive statistics and explore data with graphs
• perform analysis of variance
• perform linear regression and assess the assumptions
• use diagnostic statistics to identify potential outliers in multiple regression
• use chi-square statistics to detect associations among categorical variables
• fit a multiple logistic regression model.

Who should attend

Statisticians and business analysts who want to use a point-and-click interface to SAS

Formats available	Duration
Classroom:	3.0 days
Live Web Classroom:	6 half-day session(s)	System Requirements
e-Learning:	24 hours/180 day license	System Requirements

Before attending this course, you should

• be familiar with both SAS Enterprise Guide and basic statistical concepts
• have completed an undergraduate course in statistics covering p-values, hypothesis testing, analysis of variance, and regression
• be able to perform analyses and create data sets with SAS Enterprise Guide software. You can gain this experience by completing the SAS Enterprise Guide 1: Querying and Reporting course.

This course addresses SAS Enterprise Guide, SAS/STAT, SAS Analytics Pro software.

This course also addresses Base SAS software and touches on SAS/GRAPH and SAS/STAT software. You benefit from this course even if SAS/GRAPH software is not installed at your location.

Prerequisite Basic Concepts

• discussing descriptive statistics
• discussing inferential statistics
• listing steps for conducting a hypothesis test
• discussing basics of using your SAS software

Getting Started in Enterprise Guide 7.1

• introducing to the SAS Enterprise Guide 7.1 environment

Introduction to Statistics

• discussing fundamental statistical concepts
• examining distributions
• describing categorical data
• constructing confidence intervals
• performing simple tests of hypothesis

Analysis of Variance (ANOVA)

• performing one-way ANOVA
• performing multiple comparisons
• performing two-way ANOVA with and without interactions

Regression

• using exploratory data analysis
• producing correlations
• fitting a simple linear regression model
• understanding the concepts of multiple regression
• building and interpreting models
• describing all regression techniques
• exploring stepwise selection techniques

Regression Diagnostics

• examining residuals
• investigating influential observations and collinearity

Categorical Data Analysis

• describing categorical data
• examining tests for general and linear association
• understanding the concepts of logistic regression and multiple logistic regression
• performing backward elimination with logistic regression