The DO Loop
Statistical programming in SAS with an emphasis on SAS/IML programs
Many common probability distributions contain terms that increase or decrease quickly, such as the exponential function and factorials. The numerical evaluation of these quantities can result in numerical overflow (or underflow). This is why we often work on the logarithmic scale: on the log-scale, the numerical computations for equations such
Suppose you measure data weekly. According to the ISO standard, weeks are measured in the range 1-53, where most years have 52 weeks, but occasionally there is a "leap week." (The WEEK function in SAS implements the ISO standard to find the week-of-the-year for any date.) The heat map to
Dating can be a challenge. No, I'm not talking about the process of finding a soulmate. I'm talking about managing days, weeks, months, and years in statistical analyses and reports! One challenge is how to number the weeks of the year. Because there are seven days in a week, 52
I follow several data visualization experts on social media. Sometimes, I see a graph that I struggle to interpret. When that happens, I ask myself whether there is a simpler and more effective way to visualize the data. Recently, I saw an example of using a "horizon plot" to visualize
While researching a topic on effect sizes, I learned about a SAS function that is related to noncentrality parameters. I previously wrote an article about the noncentral t distribution, which is one of several well-known distributions that contains an optional noncentrality parameter. I mentioned that the PDF, CDF, and QUANTILE
A previous article discusses a "Catch-22" paradox for fitting nonlinear regression models: You can't estimate the parameters until you fit the model, but you can't fit the model until you provide an initial guess for the parameters! If your initial guess for the parameters is not good enough, the nonlinear