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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

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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

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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

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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)

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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

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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

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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

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Rick WicklinJune 22, 2016 24

Use the EFFECTPLOT statement to visualize regression models in SAS

Graphs enable you to visualize how the predicted values for a regression model depend on the model effects. You can gain an intuitive understanding of a model by using the EFFECTPLOT statement in SAS to create graphs like the one shown at the top of this article. Many SAS regression

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Advanced Analytics | Learn SAS $Fit a logistic model in SAS: ordinal regression, multinomial regression, Logistic model for longitudinal (or repeated measures) data, random effects models, conditional logistic regression, survey logistic regression, and fractional logistic models$

Rick WicklinMay 23, 2016 3

How to fit a variety of logistic regression models in SAS

SAS software can fit many different kinds of regression models. In fact a common question on the SAS Support Communities is "how do I fit a <name> regression model in SAS?" And within that category, the most frequent questions involve how to fit various logistic regression models in SAS. There

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Rick WicklinFebruary 3, 2016 2

Rolling statistics in SAS/IML

Last week I showed how to use PROC EXPAND to compute moving averages and other rolling statistics in SAS. Unfortunately, PROC EXPAND is part of SAS/ETS software and not every SAS site has a license for SAS/ETS. For simple moving averages, you can write a DATA step program, as discussed

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