Statistics II: ANOVA and Regression
Role
Statistical Analyst
Duration
3 days
Description
This course explains techniques for performing multiple polynomial regression analysis and analysis of variance using SAS software. The course also covers Poisson regression, gamma regression, analysis of covariance, and an introduction to linear mixed models. You learn how to write SAS programs to fit linear models and interpret output. Emphasis is on fitting models, interpreting results, verifying the model assumptions, and using alternative analysis strategies when necessary.
Who should attend
This course is designed for data analysts and researchers with some statistical training who want to analyze continuous response data by using SAS/STAT software to perform analysis of variance and regression methods. Using Poisson regression for discrete count data is also addressed.
Prerequisites
Before attending this course, you should
- have some experience creating and managing SAS data sets, which you can gain from the SAS Programming I: Essentials course
- be able to fit simple and multiple linear regression models using the REG procedure
- be able to analyze a one-way analysis of variance using the GLM procedure
- understand the statistical concepts of normal distribution, sampling distributions, hypothesis testing, and estimation
- have completed a graduate-level course in regression and analysis of variance methods or the Statistics I: Introduction to ANOVA, Regression, and Logistic Regression course.
SAS Modules Used
This course addresses the following software product(s): SAS/STAT, SAS/ETS, SAS/GRAPH. You benefit from this course even if SAS/GRAPH or SAS/ETS software are not installed at your location.
Course Topics
Regression
- building and evaluating multiple polynomial regression models
- dealing with violations of model assumptions
- using the GENMOD procedure to fit Poisson and gamma regression models
Analysis of Variance
- performing n-way ANOVA
- interpreting significant interactions
- writing CONTRAST and ESTIMATE statements
- understanding issues associated with unbalanced data
- performing linear mixed model analysis
Analysis of Covariance and Regression Using Indicator Variables
- building and interpreting analysis of covariance models using the GLM procedure
- using and interpreting indicator variables in the REG procedure
- comparing analysis of covariance with regression using indicator variables
- performing n-way ANOVA
- interpreting significant interactions
- writing CONTRAST and ESTIMATE statements
- understanding issues associated with unbalanced data
- performing linear mixed model analysis
