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Rick Wicklin RSS
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.

Analytics | Data Visualization | Learn SAS

Rick WicklinJune 19, 2019 1

Influential observations in a linear regression model: The DFFITS and Cook's D statistics

A previous article describes the DFBETAS statistics for detecting influential observations, where "influential" means that if you delete the observation and refit the model, the estimates for the regression coefficients change substantially. Of course, there are other statistics that you could use to measure influence. Two popular ones are the

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Analytics | Data Visualization | Learn SAS

Rick WicklinJune 17, 2019 11

Influential observations in a linear regression model: The DFBETAS statistics

My article about deletion diagnostics investigated how influential an observation is to a least squares regression model. In other words, if you delete the i_th observation and refit the model, what happens to the statistics for the model? SAS regression procedures provide many tables and graphs that enable you to

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Advanced Analytics | Programming Tips

Rick WicklinJune 12, 2019 5

Leave-one-out statistics and a formula to update a matrix inverse

For linear regression models, there is a class of statistics that I call deletion diagnostics or leave-one-out statistics. These observation-wise statistics address the question, "If I delete the i_th observation and refit the model, what happens to the statistics for the model?" For example: The PRESS statistic is similar to

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Learn SAS | Programming Tips

Rick WicklinJune 10, 2019 15

5 reasons to use PROC FORMAT to recode variables in SAS

Recoding variables can be tedious, but it is often a necessary part of data analysis. Almost every SAS programmer has written a DATA step that uses IF-THEN/ELSE logic or the SELECT-WHEN statements to recode variables. Although creating a new variable is effective, it is also inefficient because you have to

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Data Visualization | Learn SAS | Programming Tips

Rick WicklinJune 5, 2019 2

Plot a family of curves in SAS

A family of curves is generated by an equation that has one or more parameters. To visualize the family, you might want to display a graph that overlays four of five curves that have different parameter values, as shown to the right. The graph shows members of a family of

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Data Visualization | Learn SAS | Programming Tips

Rick WicklinJune 3, 2019 4

Graph wide data and long data in SAS

Statistical programmers and analysts often use two kinds of rectangular data sets, popularly known as wide data and long data. Some analytical procedures require that the data be in wide form; others require long form. (The "long format" is sometimes called "narrow" or "tall" data.) Fortunately, the statistical graphics procedures

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Analytics | Data Visualization | Learn SAS

Rick WicklinMay 30, 2019 10

Visualize interaction effects in regression models

Knowing how to visualize a regression model is a valuable skill. A good visualization can help you to interpret a model and understand how its predictions depend on explanatory factors in the model. Visualization is especially important in understanding interactions between factors. Recently I read about work by Jacob A.

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Analytics | Programming Tips

Rick WicklinMay 28, 2019 2

The Theil-Sen robust estimator for simple linear regression

Modern statistical software provides many options for computing robust statistics. For example, SAS can compute robust univariate statistics by using PROC UNIVARIATE, robust linear regression by using PROC ROBUSTREG, and robust multivariate statistics such as robust principal component analysis. Much of the research on robust regression was conducted in the

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Analytics | Data Visualization

Rick WicklinMay 22, 2019 1

Gershgorin discs and the location of eigenvalues

The eigenvalues of a matrix are not easy to compute. It is remarkable, therefore, that with relatively simple mental arithmetic, you can obtain bounds for the eigenvalues of a matrix of any size. The bounds are provided by using a marvelous mathematical result known as Gershgorin's Disc Theorem. For certain

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Analytics | Programming Tips

Rick WicklinMay 20, 2019 4

Critical values of the Kolmogorov-Smirnov test

Recently I wrote about how to compute the Kolmogorov D statistic, which is used to determine whether a sample has a particular distribution. One of the beautiful facts about modern computational statistics is that if you can compute a statistic, you can use simulation to estimate the sampling distribution of

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