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Rick WicklinMarch 12, 2018 1

Pi, special functions, and distributions

Welcome to my annual Pi Day post. Every year on March 14th (written 3/14 in the US), geeky mathematicians and their friends celebrate "all things pi-related" because 3.14 is the three-decimal approximation to pi. Pi is a mathematical constant that never changes. Pi is the same value today as it

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Analytics | Programming Tips

Rick WicklinMarch 7, 2018 11

Fit a distribution from quantiles

Data analysts often fit a probability distribution to data. When you have access to the data, a common technique is to use maximum likelihood estimation (MLE) to compute the parameters of a distribution that are "most likely" to have produced the observed data. However, how can you fit a distribution

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Analytics | Programming Tips

Rick WicklinMarch 5, 2018 6

The probability of a saddle point in a matrix

Many people know that a surface can contain a saddle point, but did you know that you can define the saddle point of a matrix? Saddle points in matrices are somewhat rare, which means that if you choose a random matrix you are unlikely to choose one that has a

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Analytics | Programming Tips

Solve nonlinear system of equations in SAS

Rick WicklinFebruary 28, 2018 3

Solve a system of nonlinear equations with SAS

This article shows how to use SAS to solve a system of nonlinear equations. When there are n unknowns and n equations, this problem is equivalent to finding a multivariate root of a vector-valued function F(x) = 0 because you can always write the system as f1(x1, x2, ..., xn)

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Analytics | Programming Tips

Quantiles are the solutions to the equation CDF(x)-p=0, where p is a probability

Rick WicklinFebruary 19, 2018 3

Compute the quantiles of any distribution

Your statistical software probably provides a function that computes quantiles of common probability distributions such as the normal, exponential, and beta distributions. Because there are infinitely many probability distributions, you might encounter a distribution for which a built-in quantile function is not implemented. No problem! This article shows how to

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Analytics | Learn SAS

Rick WicklinFebruary 14, 2018 13

The difference between CLASS statements and BY statements in SAS

When I first learned to program in SAS, I remember being confused about the difference between CLASS statements and BY statements. A novice SAS programmer recently asked when to use one instead of the other, so this article explains the difference between the CLASS statement and BY variables in SAS

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Analytics | Data Visualization

Rick WicklinFebruary 7, 2018 0

The distribution of shared birthdays in the Birthday Problem

If N random people are in a room, the classical birthday problem provides the probability that at least two people share a birthday. The birthday problem does not consider how many birthdays are in common. However, a generalization (sometimes called the Multiple-Birthday Problem) examines the distribution of the number of

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Analytics | Programming Tips

Rick WicklinJanuary 15, 2018 4

Data unavailable? Use the "eyeball distribution" to simulate

Last week I got the following message: Dear Rick: How can I create a normal distribution within a specified range (min and max)? I need to simulate a normal distribution that fits within a specified range. I realize that a normal distribution is by definition infinite... Are there any alternatives,

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Analytics | Data Visualization | Learn SAS

Rick WicklinDecember 20, 2017 4

How to create a sliced fit plot in SAS

I previously showed an easy way to visualize a regression model that has several continuous explanatory variables: use the SLICEFIT option in the EFFECTPLOT statement in SAS to create a sliced fit plot. The EFFECTPLOT statement is directly supported by the syntax of the GENMOD, LOGISTIC, and ORTHOREG procedures in

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Analytics | Data Visualization | Learn SAS

Visualize multivariate regression model by slicing the continuous variables. Graph created by using the EFFECTPLOT SLICEFIT statement in SAS.

Rick WicklinDecember 18, 2017 6

Visualize multivariate regression models by slicing continuous variables

Slice, slice, baby! You've got to slice, slice, baby! When you fit a regression model that has multiple explanatory variables, it is a challenge to effectively visualize the predicted values. This article describes how to visualize the regression model by slicing the explanatory variables. In SAS, you can use the

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