The DO Loop

Advanced Analytics

Move beyond spreadsheets to data mining, forecasting, optimization – and more

Advanced Analytics

Rick WicklinFebruary 8, 2017 38

Winsorization: The good, the bad, and the ugly

On discussion forums, I often see questions that ask how to Winsorize variables in SAS. For example, here are some typical questions from the SAS Support Community: I want an efficient way of replacing (upper) extreme values with (95th) percentile. I have a data set with around 600 variables and

English

Advanced Analytics

Rick WicklinFebruary 1, 2017 1

Simulate many samples from a linear regression model

In a previous article, I showed how to simulate data for a linear regression model with an arbitrary number of continuous explanatory variables. To keep the discussion simple, I simulated a single sample with N observations and p variables. However, to use Monte Carlo methods to approximate the sampling distribution

English

Advanced Analytics | Data Visualization

Rick WicklinJanuary 30, 2017 5

Automate the creation of a discrete attribute map

If you are a SAS programmer and use the GROUP= option in PROC SGPLOT, you might have encountered a thorny issue: if you use a WHERE clause to omit certain observations, then the marker colors for groups might change from one plot to another. This happens because the marker colors

English

Advanced Analytics

Rick WicklinJanuary 25, 2017 9

Simulate data for a linear regression model

This article shows how to simulate a data set in SAS that satisfies a least squares regression model for continuous variables. When you simulate to create "synthetic" (or "fake") data, you (the programmer) control the true parameter values, the form of the model, the sample size, and magnitude of the

English

Advanced Analytics

Rick WicklinJanuary 18, 2017 4

Solve mixed integer linear programming problems in SAS

This article shows how to solve mixed integer linear programming (MILP) problems in SAS. In a mixed integer problem, some of the variables in the problem are integer-valued whereas others are continuous. The objective function is a linear function of the variables and the variables can be subject to linear

English

Advanced Analytics

Locations of La Quinta Inns and Denny's restaurants

Rick WicklinJanuary 6, 2017 3

Is "La Quinta" Spanish for "Next to Denny's"?

“La Quinta” is Spanish for “next to Denny’s.” -- Mitch Hedberg, comedian Mitch Hedberg's joke resonates with travelers who drive on the US interstate system because many highway exits feature both a La Quinta Inn™ and a Denny's® restaurant within a short distance of each other. But does a

English

Advanced Analytics

Rick WicklinDecember 7, 2016 10

Simultaneous confidence intervals for a multivariate mean

Many SAS procedure compute statistics and also compute confidence intervals for the associated parameters. For example, PROC MEANS can compute the estimate of a univariate mean, and you can use the CLM option to get a confidence interval for the population mean. Many parametric regression procedures (such as PROC GLM)

English

Advanced Analytics

Rick WicklinOctober 19, 2016 0

Loess regression in SAS/IML

A previous post discusses how the loess regression algorithm is implemented in SAS. The LOESS procedure in SAS/STAT software provides the data analyst with options to control the loess algorithm and fit nonparametric smoothing curves through points in a scatter plot. Although PROC LOESS satisfies 99.99% of SAS users who

English

Advanced Analytics

Rick WicklinSeptember 14, 2016 8

Compute nearest neighbors in SAS

Finding nearest neighbors is an important step in many statistical computations such as local regression, clustering, and the analysis of spatial point patterns. Several SAS procedures find nearest neighbors as part of an analysis, including PROC LOESS, PROC CLUSTER, PROC MODECLUS, and PROC SPP. This article shows how to find

English

Advanced Analytics

Rick WicklinJuly 27, 2016 0

How to visualize a kernel density estimate

A kernel density estimate (KDE) is a nonparametric estimate for the density of a data sample. A KDE can help an analyst determine how to model the data: Does the KDE look like a normal curve? Like a mixture of normals? Is there evidence of outliers in the data? In

English

Blogs

Blogs

Advanced Analytics