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Rick WicklinApril 1, 2020 6

Estimates of doubling time for exponential growth

During an epidemic, such as the coronavirus pandemic of 2020, the media often shows graphs of the cumulative numbers of confirmed cases for different countries. Often these graphs use a logarithmic scale for the vertical axis. In these graphs, a straight line indicates that new cases are increasing at an

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Analytics | Programming Tips

Rick WicklinMarch 16, 2020 2

Predict a random integer: The tradeoff between bias and variance

Books about statistics and machine learning often discuss the tradeoff between bias and variance for an estimator. These discussions are often motivated by a sophisticated predictive model such as a regression or a decision tree. But the basic idea can be seen in much simpler situations. This article presents a

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Analytics

Rick WicklinMarch 11, 2020 7

Polygons, pi, and linear approximations

Recently, I saw a graphic on Twitter by @neilrkaye that showed the rapid convergence of a regular polygon to a circle as you increase the number of sides for the polygon. The author remarked that polygons that have 40 or more sides "all look like circles to me." That is,

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Analytics | Data Visualization

Rick WicklinFebruary 26, 2020 0

The binormal model for ROC curves

The ROC curve is a graphical method that summarizes how well a binary classifier can discriminate between two populations, often called the "negative" population (individuals who do not have a disease or characteristic) and the "positive" population (individuals who do have it). As shown in a previous article, there is

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Analytics | Data Visualization

Rick WicklinFebruary 17, 2020 2

Visualize collinearity diagnostics

A previous article shows how to interpret the collinearity diagnostics that are produced by PROC REG in SAS. The process involves scanning down numbers in a table in order to find extreme values. This can be a tedious and error-prone process. Friendly and Kwan (2009) compare this task to a

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Analytics | Learn SAS | Programming Tips

Rick WicklinFebruary 12, 2020 0

The Johnson system: Which distribution should you choose to model data?

The Johnson system (Johnson, 1949) contains a family of four distributions: the normal distribution, the lognormal distribution, the SB distribution, and the SU distribution. Previous articles explain why the Johnson system is useful and show how to use PROC UNIVARIATE in SAS to estimate parameters for the Johnson SB distribution

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Analytics | Data Visualization | Learn SAS

Rick WicklinFebruary 5, 2020 6

Visualize residual projections for linear regression

A SAS programmer wanted to create a graph that illustrates how Deming regression differs from ordinary least squares regression. The main idea is shown in the panel of graphs below. The first graph shows the geometry of least squares regression when we regress Y onto X. ("Regress Y onto X"

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Analytics | Learn SAS

Rick WicklinJanuary 29, 2020 2

Collinearity diagnostics: Should the data be centered?

In a previous article, I showed how to perform collinearity diagnostics in SAS by using the COLLIN option in the MODEL statement in PROC REG. For models that contain an intercept term, I noted that there has been considerable debate about whether the data vectors should be mean-centered prior to

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Analytics | Data Visualization | Programming Tips

Rick WicklinJanuary 27, 2020 4

The Johnson SU distribution

The Johnson system (Johnson, 1949) contains a family of four distributions: the normal distribution, the lognormal distribution, the SB distribution (which models bounded distributions), and the SU distribution (which models unbounded distributions). Note that 'B' stands for 'bounded' and 'U' stands for 'unbounded.' A previous article explains the purpose of

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Analytics | Learn SAS

Rick WicklinJanuary 23, 2020 12

Collinearity in regression: The COLLIN option in PROC REG

I was recently asked about how to interpret the output from the COLLIN (or COLLINOINT) option on the MODEL statement in PROC REG in SAS. The example in the documentation for PROC REG is correct but is somewhat terse regarding how to use the output to diagnose collinearity and how

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