There is a new version of this course. Please see SAS Enterprise Guide: ANOVA, Regression, and Logistic Regression.
This course is designed for SAS Enterprise Guide users who want to perform statistical analyses. The course is written for SAS Enterprise Guide 8 along with SAS 9.4, but students with previous SAS Enterprise Guide versions will also get value from this course. An e-learning course is also available for earlier versions.
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 | Standard duration | | |
Classroom: |
3 days | | |
|
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 the basics of using your SAS software.
Getting Started in SAS Enterprise Guide 7.1- Introducing 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.
