Last week I wrote about the 10 most popular articles from *The DO Loop* in 2016.
The popular articles tend to be about elementary topics that appeal to a wide range of SAS programmers. Today I present an "editor's choice" list of technical articles that describe more advanced statistical methods in SAS.

I've grouped the articles into three categories: statistical graphics and visualization, statistical computations, and matrix computations. If you are a SAS statistical programmer, these articles deserve a second look.

*Ten posts from The DO Loop that deserve a second look #SASTip*

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### Statistical graphics and visualization

SAS ODS graphics provides an easy way to create standard graphs for data analysis. The graphs in this list are more sophisticated:

- Have you ever struggled to specify the order and colors of categorical variables? This article provides a general technique that is often useful: insert special observations at the top of the data before you create the graph. A related technique is to append observations at the bottom of the data so that you can visually represent special values in a graph.
- If you want to visualize complex regression models in SAS, you MUST learn about the EFFECTPLOT statement! An effect plot shows the predicted response as a function of certain covariates while other covariates are held constant.
- For time-varying processes and iterative methods, animation is a valuable visualization technique. Learn how to create an animated GIF is SAS by using the BY statement in PROC SGPLOT.

### Statistical computations

These article show helpful statistical techniques that you should know about:

- Confidence intervals are an essential tool in inferential statistics. This article uses simulation to answer the question "What are confidence intervals?" A related post shows how to compute confidence intervals for a multivariate mean.
- Most SAS procedures include a CLASS statement for handling the analysis of discrete classification variables. However, for some advanced statistical methods, you might need to generate a design matrix, which is a set of variables that represent categorical variables and interactions in a regression model. This article describes four SAS procedures that can generate a design matrix.
- Some statistical algorithms (such as clustering) rely on computing the distance from an observation to its nearest neighbor. This article shows how to compute nearest neighbors in SAS. A related article shows how to compute the distances between observations in one group and observations in a different group.

### Matrix computations

The SAS DATA step is awesome. For many programming tasks, it is an efficient and effective tool. However, advanced analytical algorithms and multivariate statistics often require matrix-vector computations, which means programming in the SAS/IML language.

- SAS/IML 14.1 introduced packages. Packages are a new way to share SAS/IML programs. You can watch a video presentation about how to use and create packages.
- Statistical programmers often use the SAS/IML language to run custom optimizations. To help you navigate common pitfalls, this article presents a checklist of 10 tips to ensure that your optimizations are correct and efficient.
- SAS/IML is often used to carry out efficient simulation and bootstrap studies. This article describes how to implement the smooth bootstrap method in SAS/IML.
- Because SAS/IML is a matrix language, it is the ideal environment to implement Markov transition matrices. A related article shows that certain probabilities in Markov chains can be computed in terms of properties of the transition matrix.

There you have it, 10 articles from *The DO Loop* in 2016 that I think are worth a second look. Did I omit your favorite article? Leave a comment.

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