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.
As announced and demonstrated at SAS Innovate 2024, SAS plans to include a generative AI assistant called SAS Viya Copilot in the forthcoming SAS Viya Workbench. You can submit a text prompt (by putting it in a comment string) and the Copilot will generate SAS code for you. My colleagues
While reviewing a book on numerical analysis, I was reminded of a classic interpolation problem. Suppose you have n pairs of points in the plane: (x1,y1), (x2,y2), ..., (xn,yn), where the first coordinates are distinct. Then you can construct a unique polynomial of degree (at most) n-1 that passes through
One of the most exciting features of SAS Viya Workbench is that the code editor includes a generative AI component called SAS Viya Copilot. This feature was announced and demonstrated at SAS Innovate 2024. With the Copilot, you can specify a text prompt that generates SAS code. For example, you
This article discusses how to scale a probability density curve so that it fits appropriately on a histogram, as shown in the graph to the right. By definition, a probability density curve is scaled so that the area under the curve equals 1. However, a histogram might show counts or
A previous article discusses a formula for a confidence interval for R-square in a linear regression model (Olkin and Finn (1995) "Correlations redux", Psychological Bulletin) The formula is useful for large data sets, but should be used with caution for small samples. At the end of the previous article, I
A SAS analyst ran a linear regression model and obtained an R-square statistic for the fit. However, he wanted a confidence interval, so he posted a question to a discussion forum asking how to obtain a confidence interval for the R-square parameter. Someone suggested a formula from a textbook (Cohen,
A SAS analyst read my previous article about visualizing the predicted values for a regression model that uses spline effects. Because the original explanatory variable does not appear in the model, the analyst had several questions: How do you score the model on new data? The previous example has only
Sometimes labels for variables get "dropped" during data preparation and cleaning. One example is when data are transposed from "wide form" to "long form." For example, suppose a data set has three variables, X, Y, and Z, each with labels. If you transpose the data to long form, the new
A SAS programmer wanted to visualize density estimate for some univariate data. The data had several groups, so he wanted to create a panel of density estimate, which you can easily do by using PROC SGPANEL in SAS. However, the programmer's boss wanted to see filled density estimates, such as
After writing a program that simulates data, it is important to check that the statistical properties of the simulated (synthetic) data match the properties of the model. As a first step, you can generate a large random sample from the model distribution and compare the sample statistics to the expected
A SAS programmer was trying to implement an algorithm in PROC IML in SAS based on some R code he had seen on the internet. The R code used the rank() and order() functions. This led the programmer to ask, "What is the different between the rank and the order?
A SAS statistical programmer recently asked a theoretical question about statistics. "I've read that 'p-values are uniformly distributed under the null hypothesis,'" he began, "but what does that mean in practice? Is it important?" I think data simulation is a great way to discuss the conditions for which p-values are
At a recent conference in Las Vegas, a presenter simulated the sum of two dice and used it to simulate the game of craps. I write a lot of simulations, so I'd like to discuss two related topics: How to simulate the sum of two dice in SAS. This is
Years ago, I wrote an article that showed how to visualize patterns of missing data. During a recent data visualization talk, I discussed the program, which used a small number of SAS IML statements. An audience member asked whether it is possible to construct the same visualization by using only
A SAS programmer wanted to estimate a proportion and a confidence interval (CI), but didn't know which SAS procedure to call. He knows a formula for the CI from an elementary statistics textbook. If x is the observed count of events in a random sample of size n, then the
In a recent article, I graphed the PDF of a few Beta distributions that had a variety of skewness and kurtosis values. I thought that I had chosen the parameter values to represent a wide variety of Beta shapes. However, I was surprised to see that the distributions were all
The moment-ratio diagram is a tool that is useful when choosing a distribution that models a sample of univariate data. As I show in my book (Simulating Data with SAS, Wicklin, 2013), you first plot the skewness and kurtosis of the sample on the moment-ratio diagram to see what common
A SAS programmer wanted to simulate samples from a family of Beta(a,b) distributions for a simulation study. (Recall that a Beta random variable is bounded with values in the range [0,1].) She wanted to choose the parameters such that the skewness and kurtosis of the distributions varied over range of
A dot plot is a standard statistical graphic that displays a statistic (often a mean) and the uncertainty of the statistic for one or more groups. Statisticians and data scientists use it in the analysis of group data. In late 2023, I started noticing headlines about "dot plots" in the
Recently, I saw a scatter plot that displayed the ticks, values, and labels for a vertical axis on the right side of a graph. In the SGPLOT procedure in SAS, you can use the Y2AXIS option to move an axis on the right side of a graph. Similarly, you can
A recent article describes how to estimate coefficients in a simple linear regression model by using maximum likelihood estimation (MLE). One of the nice properties of an MLE formulation is that you can compare a large model with a nested submodel in a natural way. For example, if you can
A statistical analyst used the GENMOD procedure in SAS to fit a linear regression model. He noticed that the table of parameter estimates has an extra row (labeled "Scale") that is not a regression coefficient. The "scale parameter" is not part of the parameter estimates table produced by PROC REG
Happy Pi Day! Every year on March 14th (written 3/14 in the US), people in the mathematical sciences celebrate all things pi-related because 3.14 is the three-decimal approximation to π ≈ 3.14159265358979.... Pi is a mathematical constant defined as the ratio of a circle's circumference (C) to its diameter (D).
I recently wrote about the Number-Word Game, which is an iterative algorithm that generates a sequence of natural numbers by using the lengths of the words for the numbers. In English, the words are "one", "two", "three", and so on. You can play the Number-Word Game in any alphabetic language
Have you heard about the Number-Word Game? This is a simple game that has the following rules: Start with any positive integer. Write down the English word for the integer. Count the number of letters in the word. This gives a new positive integer. Go to (2). Repeat until a
I sometimes see analysts overuse colors in statistical graphics. My rule of thumb is that you do not need to use color to represent a variable that is already represented in a graph. For example, it is redundant to use a continuous color ramp to represent the lengths of bars
With four parameters I can fit an elephant. With five I can make his trunk wiggle. — John von Neumann Ever since the dawn of statistics, researchers have searched for the Holy Grail of statistical modeling. Namely, a flexible distribution that can model any continuous univariate data. As the quote
In statistical quality control, practitioners often estimate the variability of products that are being produced in a manufacturing plant. It is important to estimate the variability as soon as possible, which means trying to obtain an estimate from a small sample. Samples of size five or less are not uncommon
In a recent Monte Carlo project, I needed to simulate numbers on an interval by using a continuous linear probability density function (PDF). An example is shown to the right. In this example, the linear density function is decreasing on the interval, but the function could also be constant or
I read a journal article in which a researcher used a formula for the probability density function (PDF) of the sample correlation coefficient. The formula was rather complicated, and presented with no citation, so I was curious to learn more. I found the distribution for the correlation coefficient in the
