New Year's resolutions for my blog


It's a New Year and I'm ready to make some resolutions. Last year I launched this blog with my Hello, World post in which I said:

In this blog I intend to discuss, describe, and disseminate ideas related to statistical programming with the SAS/IML language.... I will present tips and techniques for writing efficient SAS/IML programs for data analysis, modeling, simulation, sampling, matrix computations, regression, and data visualization.

I think I've stayed on task, and I've tried to mix in some entertaining and amusing posts as well. For example, several of my most-read posts in 2010 are statistical analysis of whimsical topics, such as the probability that my grocery bill will be a whole-dollar amount.

In addition to continuing to present "tips and techniques for writing efficient SAS/IML programs," here are my resolutions for my blog in 2011:

  1. 100 blog posts in 2011: I aspire to post 100 articles in 2011.
  2. Provide Content: Some blogs are primarily aggregators: a typical post links to content written by someone else. In The DO Loop, I resolve to provide fresh, original content in most posts.
  3. Promote Discussions: As I said in a recent SAS Press interview, "a book is a monologue; a blog is a dialogue." In 2011, I intend to occasionally post a question, issue, or challenge that promotes discussion between my readers and me.
  4. Learn a new area of statistics: There are several areas of statistics that I should learn more about. This year I resolve to learn a new statistical topic and to blog about my successes and frustrations—accompanied by SAS/IML code, of course!

Is there anything that you'd like to see me do more of (or less of) in the New Year? I'm listening.


About Author

Rick Wicklin

Distinguished Researcher in Computational Statistics

Rick Wicklin, PhD, is a distinguished researcher in computational statistics at SAS and is a principal developer of SAS/IML software. His areas of expertise include computational statistics, simulation, statistical graphics, and modern methods in statistical data analysis. Rick is author of the books Statistical Programming with SAS/IML Software and Simulating Data with SAS.


  1. I second the original content. It is annoying to read sites that are just copies of someone else's ideas.

    If you have any ideas for teaching and making it fun and interesting, I'm always struggling with that. I love statistics but everyone from my 12-year-old to my graduate students tends to roll their eyes when I say that.

    I also like your learning new statistics idea. There seems to be a misconception that there are people who are "good at math" and some how "get it" effortlessly and people who are bad at math. Of course, nothing could be further from the truth.

  2. Well, there's an open-ended question! When I was a professor, my primary mantra was "math is not a spectator sport." I would try to do whatever I could to involve the students and enable them to "own" the material, rather than just watching me write equations on the board. When I teach stats, I always start with data and pose a problem to prevent the "why should I care" questions.

    Who else out there teaches? What are your tips for making statistics fun and interesting?

  3. Oh yes, discussion of pedagogy -- you're playing my favorite song! (not that I'm such a good singer, I just love to think of what helps people learn statistics better)

    I have to admit to being a hack, but my gold standard for good teaching has long been a friend of mine who is a professor over at NCSU (Dave Dickey, for those of you who know him). I've watched him teach and taught with him many times over the years, and I've observed one thing he does with every new concepts he introduces:

    1. Present a simple, folksy analogy with no math. Talk about visiting Mars, or a mother snake, or earthquake-resistant buildings in San Francisco, or something that gets people's imaginations warmed up.
    2. Dive into all the math of what you're teaching.
    3. Present the applied statistical concept in a data-specific context to show why it works.

    I've tried to use this approach in discussing new concepts and the beauty of it is that everyone in the room can respond to at least one of the 3 explanations.

    So I'm a hack because I borrowed that strategy. but at least I'm borrowing from one of the best.

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