This outline is provisional and subject to change.
This short course continues the learnings from the Learning Statistics with SAS course and introduces how to analyze continuous response data and discrete count data. It teaches how to apply Polynomial regression, Poisson regression and negative binomial regression.
Please note: This is a taster course providing an overview of the listed SAS tool(s) and capabilities. For more comprehensive training, please consider the Statistics 2: ANOVA and Regression course. Attendees who complete this short course and wish to complete the associated comprehensive training may be eligible for a discount.
Learn how to
- 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
This outline is provisional and subject to change.
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 OR Learning with Statistics course.Students should have completed the SAS® Programming 1: Essentials and Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression courses or have equivalent experience.
This course addresses SAS/STAT software.
This outline is provisional and subject to change.
Polynomial Regression- building and evaluating simple polynomial regression models using PROC GLMSELECT
- Dealing with multicollinearity in polynomial regression
- Modelling non-linear relationships
Regression Diagnostics and Remedial Measures- dealing with violations of model assumptions and multicollinearity
Generalized Linear Models- using the GENMOD procedure to fit Poisson and negative binomial regression models
