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.

Apprendre à

• 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.

A qui s’adresse cette formation ?

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

Formats de formation disponibles	Durée standard (peut varier selon les sessions. Se référer au calendrier)
Formations présentielles:	3 jours
Formations à distance:	6 sessions d'une demi-journée chacune
e-Learning:	21 heures/180 jour licence

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 : utilisation de l'interface course.

Cette formation concerne SAS Enterprise Guide, SAS/STAT, SAS Analytics Pro logiciel

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.

Course Overview and Data Setup

• Introducing the course.
• Setting up the course data.

Getting Started in SAS Enterprise Guide 8

• Introducing the SAS Enterprise Guide 8 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.

Resources

• Exploring additional resources.