When does class start/end?
Classes begin promptly at 9:00 am, and typically end at 5:00 pm.
Statistics 2: ANOVA and Regression
This course teaches you how to analyze continuous response data and discrete count data. Linear regression, Poisson regression, negative binomial regression, gamma regression, analysis of variance,...
Read MoreThis course teaches you how to analyze continuous response data and discrete count data. Linear regression, Poisson regression, negative binomial regression, gamma regression, analysis of variance, linear regression with indicator variables, analysis of covariance, and mixed models ANOVA are presented in the course.
Skills Gained
- Fit polynomial regression models using the GLMSELECT and REG procedures.
- Select models based on several statistics and automatic model selection methods using PROC GLMSELECT.
- Evaluate model fit and model assumptions using the GLMSELECT, REG, GLM, GENMOD, and UNIVARIATE procedures.
- Fit Poisson and negative binomial models using the GENMOD procedure, and fit gamma regression models using the GLIMMIX procedure.
- Perform analysis of variance using the GLM procedure.
- Write LSMESTIMATE statements in PROC GLM.
- Fit ANCOVA models using PROC GLM.
- Fit models with random effects using PROC GLIMMIX.
- Create a variety of statistical graphs.
Who Can Benefit
- Data analysts and researchers with some statistical training
Prerequisites
- Before attending this course, you should:
- Have some experience creating and managing SAS data sets, which you can gain from the SAS® Programming 1: 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 1: Introduction to ANOVA, Regression, and Logistic Regression course.
Course Details
Multiple Linear Regression
- Review of general linear models.
- Simple polynomial regression.
- Polynomial regression and multicollinearity.
- Modeling nonlinear relationships.
Regression Diagnostics and Remedial Measures
- Regression model diagnostics.
- Remedial measures.
Analysis of Variance
- ANOVA review.
- Postfitting analyses.
- Evaluations of model assumptions and remedial measures.
Analysis of Covariance
- Introduction to analysis of covariance (ANCOVA).
- Least squares means for ANCOVA models.
- Diagnostics and remedial measures for ANCOVA models.
Introduction to Generalized Linear Models
- Introduction to generalized linear models.
- Poisson regression and negative binomial regression.
- Introduction to gamma regression.