13 popular articles from 2013


In 2013 I published 110 blog posts. Some of these articles were more popular than others, often because they were linked to from a SAS newsletter such as the SAS Statistics and Operations Research News. In no particular order, here are some of my most popular posts from 2013, organized into categories. I've included two posts from December of 2012 because December articles rarely get a chance to appear on these "Best of" lists.

Fun articles

Although I generally write about statistical topics and programming, sometimes I just like to have a little fun:

  • Like fractals? Like Christmas? Then you will enjoy seeing the fractal Christmas tree that I created for Christmas 2012.
  • Do you know how old your version of SAS is? Read the main article to discover the dates of SAS releases going back to SAS version 8.0, and read the comments to go back in time to 1972!

The International Year of Statistics

To celebrate 2013 as the International Year of Statistics, I wrote several historical articles:


As the preceding titles indicate, often "popular" is correlated with "less technical." But not always. In 2013 I published Simulating Data with SAS, and several of my more popular articles dealt with how to efficiently carry out simulation with SAS software.

Statistical Graphics and Data Analysis

Statistical data analysis and graphics are the "bread and butter" of my blog. Here are a few articles that attracted more readers than usual.

Start your new year by (re-)reading some of these popular posts from 2013. Next week I'll resume posting new articles on topics in statistics, programming, graphics, and simulation. Happy New Year to all my readers!


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. Pingback: The best articles of 2013: Twelve posts from The DO Loop that merit a second look - The DO Loop

  2. Rick Wicklin, Bonjour,

    En fait, moi je travaille sur un projet qui porte sur la création d'une base de données pour des profils assurés.

    Je vous explique le principe :

    Dans la base il y a la variable formule qui a 3 observations (F1, F2, F3), sexe (M, F), Département (75, 78, 92) et salaire (3000, 2500, 1600).

    Ainsi, dans la base au niveau de la variable formule, on dit que par exemple : la F1 représente 20% des lignes de la base, F2 30% et F3 50% des lignes de la base sur la variable formule.
    Pour le sexe, les hommes (M) sont de 60% et les femmes (F) 40% des lignes de la base sur la variable sexe.
    Pour le Département, on a le 75 est de 50%, le 78 est 25% et le 92, 25% des lignes de la base sur la variable de la variable Département.
    Enfin pour le salaire : ceux qui gagnent 3000 $ sont de 40%, 2500 $ sont de 15% et 1600 $ de 35%.

    Le nombre d'observation souhaitée est de 5000 obs.

    Merci pour votre aide.

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