This module provides an introduction to using the SAS System. This powerful software provides the foundation for statistics instruction in the module set, so students must learn to navigate through common tasks, and manage their programs and data.
Choose Your Analysis Design
Experimental design and scientific objectives determine how a statistical analysis should be conducted. The purpose of this module is to teach students how to identify designs. Once a choice is identified, this module leads the user to one of the 49 analysis modules for performing the correct statistical analysis.
ANOVA (49 analysis modules)
Use this tab if you know what design you will use for analysis. Direct access to the 49 analysis of variance modules is provided, from a 7 by 7 table of experimental designs and treatment designs.
DAWG instructs you that testing for outliers, equal variance and normality in your dataset is essential for correct statistical results. Each analysis module links to this module at its end.
There is a separate module for analysis of variance and regression, since these have some unique features.
If you determine that your dataset contains outliers, unequal variance and/or isn't normally distributed, the Transformations page will assist you to transform your data (part of the Diagnostics module).
This module teaches "post-ANOVA" methods, how to compare treatments after the analysis of variance, in order to identify specifically why treatments differed. Two general methods are covered, mean separation and contrasts.
This tab leads you to the various regression analysis modules. Here independent variables are used to explain the response of interest, instead of treatment factors as with analysis of variance. But like the ANOVA modules, the regression modules will lead you through the steps of fitting a regression equation to your data, and then evaluating diagnostic information for problems at the end.
Worked examples illustrating specific features are provided. For example, if you want more details about what an outlier looks like and what to do about it, click on that example and use the provided SAS program and output to learn about that aspect of the statistical analysis.