• your research goals and assist you in identifying aspects of your research that may benefit from the application of statistical principles 
• the design of experiments; e.g., identification of response variables, factors, and type of statistical design appropriate for your research. 
• mechanisms for collecting, entering (e.g., Excel) and handling data prior to statistical analysis. 

• assist in managing and processing data. 
• suggest appropriate statistical methods for data analysis. 
• obtain data from client, perform appropriate statistical analyses. 

• recommend methods of data presentation, portrayal of results. 
• facilitate graphical display of data by pre-processing your data and generating transportable formats like png. 
• generate presentation graphics for clients as needed. 

Most of our consulting work is done using SAS. We also have experience with SPSS (Curt), JMP (Curt) and R (Statistics Graduate Students). 

Our experience with statistical methods is fairly broad, but certain methods comprise a lot of our consulting efforts. These include: 

• regression (linear, nonlinear, logistic) 
• ANOVA including repeated measures and mixed models 
• discrete data analysis methods (e.g., frequency tables with chi-square tests) 
• multivariate methods 

Recent years have seen increases in the use of methods such as Generalized Linear Mixed Models (GLMMs), resampling techniques (bootstrapping, permutation tests and Monte Carlo methods) and graphical methods/visualization techniques. If you are interested in assistance with areas not listed, feel free to check with us anyway -- we have used many other methods not listed and we are always looking to broaden our experiences. 

Members of the Statistical Consulting Service are interested in collaborative work with researchers in virtually any discipline. Examples of such projects include but are not limited to: 

• reviewing the exposition of statistical results in proposals and manuscripts - previously this has included reviewing papers by faculty from various departments.
• contributing to collaborative projects throughout the duration of the research process. This could include visiting the laboratory or research site to better understand the research project.

Members of Statistical Consulting have been included as coauthors on manuscripts when our contributions were deemed worthy of inclusion.  We agree to co-authorship only if we believe we have contributed significantly to the content and the primary authors offer to include us. There are many other instances where our efforts have been acknowledged.

Contacts 

Curt Doetkott: Quentin Burdick Building 242M, 701-231-7031