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Using SAS Choose Design ANOVA Compare Means Regression Examples
Why are we building this guide?

Our experience has been that scientists struggle with statistics, in particular experimental design and corresponding statistical analysis of research data.  Our goal with DAWG is to provide a teaching resource that reduces some of this struggle, by providing a guide that steps researchers through choosing designs and analyzing the resulting data.  The target audience is anyone who must statistically work with experiments, across academics and industry, from graduate students to independent scientists.

Statistics is a tool that takes practice and experience to use correctly, two things that researchers generally do not have time to obtain.  However, the continued improvements in statistical software make it tempting, sometimes out of necessity, for researchers to make their own design and analysis choices.  SAS(R) software dominates agricultural research, our area of application, and we have seen the difficulty that researchers have using SAS. 

This led to the idea of danda.sas, a collection of SAS macros that reduces paragraphs of computer commands down to two lines, with automatic inclusion of the options and diagnostics needed for correct statistical analysis of data.  Researchers now should need much less practice with SAS, and do not need to relearn software options with each new version, for example.  And with the availability of danda.sas, it was a logical next step to build DAWG, which guides researchers through the use of danda.sas for analysis of data, and also provides resources to assist with choosing experimental designs (hopefully the design is chosen first!).


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