Training

SAS Enterprise Guide: ANOVA, Regression, and Logistic Regression

e-Course

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e-Learning: 24 hours/1-yr license System Requirements View Demo Buy

This course is designed for SAS Enterprise Guide users who want to perform statistical analyses.

The classroom offerings are taught using SAS Enterprise Guide 4.2. The e-learning version uses 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.

Prerequisites

Before attending this course, you should:

  • 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 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. The course was written for Enterprise Guide 4.2 along with SAS 9.2, but students with Enterprise Guide 4.1 and SAS 9.1.3 will also get value from this course.

Course outline

Introduction to Statistics

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

Analysis of Variance

  • 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

Categorical Data Analysis

  • describing categorical data
  • examining tests for general and linear association
  • understanding the concepts of logistic regression and multiple logistic regression
  • exploring logit plots (Self-Study)

Regression Diagnostics

  • examining residuals
  • investigating influential observations and collinearity