ANOVA, Regression & Logistic Regression using SAS®

Role

Statistical Analyst

Duration

3 days

Description

This course is designed for SAS users with statistical experience who wish to perform statistical analyses using various SAS procedures. The course covers a range of statistical topics including statistical inference, analysis of variance, multiple regression, categorical data analysis, and logistic regression.

Prerequisites

Before attending this course, you should: Be able to execute SAS programs and create SAS data sets. Have an understanding of statistics including: p-values, hypothesis testing, analysis of variance and regression analysis, probably gained from an undergraduate course in statistics. This knowledge can be gained by attending a SAS Essentials: An Introduction to SAS Programming course.

SAS Modules Used

SAS/STAT®, SAS/GRAPH®.

Course Topics

Introduction to Statistics:

• Examining data distributions
• Obtaining and interpreting sample statistics using the UNIVARIATE and MEANS procedures
• Constructing confidence intervals, Performing simple hypothesis tests

Analysis of Variance:

• Performing one-way analysis of variance with the GLM procedure
• Performing multiple comparisons
• Performing two-way ANOVA with and without interactions

Regression:

• Producing scatter plots with the GPLOT procedure
• Producing correlations with the CORR procedure
• Fitting a simple linear regression model with the REG procedure
• Understanding the concepts of multiple regression
• Building and interpreting models

Regression Diagnostics:

• Examining residuals
• Investigating influence and co linearity

Categorical Data Analysis:

• Describing categorical data
• Producing frequency tables with the FREQ procedure
• Examining tests for general and linear association
• Understanding the concepts of logistic regression
• Fitting a logistic regression model using the LOGISTIC procedure

Objectives

After attending this course, you will be able to: Construct graphs to explore and summarise data, Construct confidence intervals for means and test hypotheses, Perform one-way and two-way analysis of variance, Apply multiple comparison techniques, Fit simple and multiple linear regression models, Use diagnostic statistics in multiple regression, Summarise and perform analyses of categorical data.