Converting a program from one language to another can be a challenge. Even if the languages share many features, there is often syntax that is valid in one language that is not valid in another. Recently, a SAS programmer was converting a program from R to SAS IML. He reached
In several previous articles, I've shown how to use SAS to fit models to data by using maximum likelihood estimation (MLE). However, I have not previously shown how to obtain standard errors for the estimates. This article combines two previous articles to show how to obtain MLE estimates and the
A previous article shows how to use Monte Carlo simulation to approximate the sampling distribution of the sample mean and sample median. When x ~ N(0,1) are normal data, the sample mean is also normal, and there are simple formulas for the expected value and the standard error of the
An elementary course in statistics often includes a discussion of the sampling distribution of a statistic. The canonical example is the sampling distribution of the sample mean. For samples of size n that are drawn from a normally distribution (X ~ N(μ, σ)), the sample mean is normally distributed as
A previous article discusses the birthday problem and its generalizations. The classic birthday problem asks, "In a room that contains N people, what is the probability that two or more people share a birthday?" The probability is much higher than you might think. For example, in a room that contains
The birthday-matching problem (also called the birthday paradox or simply the birthday problem), is a classic problem in probability. Simply stated, the birthday-matching problem asks, "If there are N people in a room, what is the chance that two of them have the same birthday?" The problem is sometimes called
The documentation for Python's SciPy package provides a table that concisely summarizes functions that are associated with continuous probability distributions. This article provides a similar table for SAS functions. For more information on the CDF, PDF, quantile, and random-variate functions, see "Four essential functions for statistical programmers." SAS functions for
Many SAS procedures support a BY statement that enables you to perform an analysis for each unique value of a BY-group variable. The SAS IML language does not support a BY statement, but you can program a loop that iterates over all BY groups. You can emulate BY-group processing by
There are many ways to model a set of raw data by using a continuous probability distribution. It can be challenging, however, to choose the distribution that best models the data. Are the data normal? Lognormal? Is there a theoretical reason to prefer one distribution over another? The SAS has
There are dozens of common probability distributions for a continuous univariate random variable. Familiar examples include the normal, exponential, uniform, gamma, and beta distributions. Where did these distributions come from? Well, some mathematician needed a model for a stochastic process and wrote down the equation for the distribution, typically by
