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.
I previously wrote about an intriguing math puzzle that involves 5-digit numbers with certain properties. This post presents my solution in the SAS/IML language. It is easy to generate all 5-digit perfect squares, but the remainder of the problem involves looking at the digits of the squares. For this reason,
A few sharp-eyed readers questioned the validity of a technique that I used to demonstrate two ways to solve linear systems of equations. I generated a random n x n matrix and then proceeded to invert it, seemingly without worrying about whether the matrix even has an inverse! I responded to the
I was intrigued by a math puzzle posted to the SAS Discussion Forum (from New Scientist magazine). The problem is repeated below, but I have added numbers in brackets because I am going to comment on each clue: [1] I have written down three different 5-digit perfect squares, which [2]
I showed a SAS/IML customer a debugging tip, and she said that I should blog about it because she had never seen it before. The tip is very simple: inside of a DO loop, use the MOD function to selectively print the values of variables. Recall that the expression MOD(a,b)
In my previous post, I blogged about how to sample from a finite mixture distribution. I showed how to simulate variables from populations that are composed of two or more subpopulations. Modeling a response variable as a mixture distribution is an active area of statistics, as judged by many talks
Sometimes a population of individuals is modeled as a combination of subpopulations. For example, if you want to model the heights of individuals, you might first model the heights of males and females separately. The height of the population can then be modeled as a combination of the male and
The other day I encountered a SAS Knowledge Base article that shows how to count the number of missing and nonmissing values for each variable in a data set. However, the code is a complicated macro that is difficult for a beginning SAS programmer to understand. (Well, it was hard
Last week I showed a graph of the number of US births for each day in 2002, which shows a strong day-of-the-week effect. The graph also shows that the number of births on a given day is affected by US holidays. This blog post looks closer at the holiday effect.
Polynomials are used often in data analysis. Low-order polynomials are used in regression to model the relationship between variables. Polynomials are used in numerical analysis for numerical integration and Taylor series approximations. It is therefore important to be able to evaluate polynomials in an efficient manner. My favorite evaluation technique
My friend Chris posted an analysis of the distribution of birthdays for 236 of his Facebook friends. He noted that more of his friends have birthdays in April than in September. The numbers were 28 for April, but only 25 for September. As I reported in my post on "the
