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.