# The Last Statistical Model You’ll Ever Need in SAS

Use generalized linear mixed models to replace t-tests, ANOVA, regression, and more!

This course will teach you how to use the generalized linear mixed model framework to replicate (and go beyond) the classical statistical tests such as t-test, ANOVA, and regression in SAS. In the first lecture, I start by asking why use generalized linear mixed models (GLMMs)? Next, I explain how to get and use a free version of SAS, SAS Studio. The second lecture goes over the details of GLMMs. This includes definitions, model examples, parts, assumptions, and caveats. The section ends with a quick example in SAS. Lecture three covers how to run GLMMs in SAS using PROC GLIMMIX. It starts with an overview of the procedure, then delves into the core statements, other statements, and common statement options. Lecture four focuses on the output table and datasets produced by PROC GLIMMIX. The first half covers table interpretation. The second half wrangles data outputs to graph bar plots and scatterplots. Lectures five through ten go through side-by-side examples of standard statistical tests compared to generalized linear mixed models, using the same datasets. Model specification and output interpretation is considered for each. The tests covered are: Chi-Square Test of Independence, Two-Way T-test, One-Way T-test, Paired T-test, One-Way ANOVA, Two-Way ANOVA, Repeated-Measures ANOVA, ANCOVA, Simple Linear Regression, Multiple Linear Regression, Logistic Regression (Categorical), Logistic Regression (Numerical), Poisson Regression, Linear Mixed Models, and Generalized Linear Mixed Models proper. In the eleventh and final lecture, I explain what tests you can’t do with a GLMM format, provide next steps to take your learning, and list useful references. By the end of the course, you will be confidently using GLMMs as a new paradigm for statistical analysis.

What you’ll learn

• Understand what Generalized Linear Mixed Models (GLMMs) are.
• See how they can be used to fit most other statistical models (T-tests, ANOVA, Regression, etc.).
• Learn how to construct, run, and interpret GLMMs in SAS using PROC GLIMMIX.
• Test side-by-side comparisons of statistical models with PROC GLIMMIX.

Course Content

• Theory –> 4 lectures • 52min.
• Practice –> 7 lectures • 1hr 51min. Requirements

This course will teach you how to use the generalized linear mixed model framework to replicate (and go beyond) the classical statistical tests such as t-test, ANOVA, and regression in SAS. In the first lecture, I start by asking why use generalized linear mixed models (GLMMs)? Next, I explain how to get and use a free version of SAS, SAS Studio. The second lecture goes over the details of GLMMs. This includes definitions, model examples, parts, assumptions, and caveats. The section ends with a quick example in SAS. Lecture three covers how to run GLMMs in SAS using PROC GLIMMIX. It starts with an overview of the procedure, then delves into the core statements, other statements, and common statement options. Lecture four focuses on the output table and datasets produced by PROC GLIMMIX. The first half covers table interpretation. The second half wrangles data outputs to graph bar plots and scatterplots. Lectures five through ten go through side-by-side examples of standard statistical tests compared to generalized linear mixed models, using the same datasets. Model specification and output interpretation is considered for each. The tests covered are: Chi-Square Test of Independence, Two-Way T-test, One-Way T-test, Paired T-test, One-Way ANOVA, Two-Way ANOVA, Repeated-Measures ANOVA, ANCOVA, Simple Linear Regression, Multiple Linear Regression, Logistic Regression (Categorical), Logistic Regression (Numerical), Poisson Regression, Linear Mixed Models, and Generalized Linear Mixed Models proper. In the eleventh and final lecture, I explain what tests you can’t do with a GLMM format, provide next steps to take your learning, and list useful references. By the end of the course, you will be confidently using GLMMs as a new paradigm for statistical analysis.

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