Posts Tagged ‘ Statistical Programming ’

3 ways to obtain the Hessian at the MLE solution for a regression model

February 13, 2019
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3 ways to obtain the Hessian at the MLE solution for a regression model

When you use maximum likelihood estimation (MLE) to find the parameter estimates in a generalized linear regression model, the Hessian matrix at the optimal solution is very important. The Hessian matrix indicates the local shape of the log-likelihood surface near the optimal value. You can use the Hessian to estimate

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4 reasons to use PROC PLM for linear regression models in SAS

February 11, 2019
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4 reasons to use PROC PLM for linear regression models in SAS

Have you ever run a regression model in SAS but later realize that you forgot to specify an important option or run some statistical test? Or maybe you intended to generate a graph that visualizes the model, but you forgot? Years ago, your only option was to modify your program

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Simulate data for a regression model with categorical and continuous variables

January 28, 2019
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Simulate data for a regression model with categorical and continuous variables

This article shows how to use SAS to simulate data that fits a linear regression model that has categorical regressors (also called explanatory or CLASS variables). Simulating data is a useful skill for both researchers and statistical programmers. You can use simulation for answering research questions, but you can also

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Coding and simulating categorical variables in regression models

January 23, 2019
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Coding and simulating categorical variables in regression models

Recently I was asked to explain the result of an ANOVA analysis that I posted to a statistical discussion forum. My program included some simulated data for an ANOVA model and a call to the GLM procedure to estimate the parameters. I was asked why the parameter estimates from PROC

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Create training, validation, and test data sets in SAS

January 21, 2019
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Create training, validation, and test data sets in SAS

In machine learning and other model building techniques, it is common to partition a large data set into three segments: training, validation, and testing. Training data is used to fit each model. Validation data is a random sample that is used for model selection. These data are used to select

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Three ways to add a line to a Q-Q plot

January 16, 2019
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Three ways to add a line to a Q-Q plot

A quantile-quantile plot (Q-Q plot) is a graphical tool that compares a data distribution and a specified probability distribution. If the points in a Q-Q plot appear to fall on a straight line, that is evidence that the data can be approximately modeled by the target distribution. Although it is

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10 posts from 2018 that deserve a second look

January 9, 2019
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10 posts from 2018 that deserve a second look

Numbers don't lie, but sometimes they don't reveal the full story. Last week I wrote about the most popular articles from The DO Loop in 2018. The popular articles are inevitably about elementary topics in SAS programming or statistics because those topics have broad appeal. However, I also write about

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When is a histogram not a histogram? When it’s a table!

December 5, 2018
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When is a histogram not a histogram? When it’s a table!

Recently a SAS programmer wanted to obtain a table of counts that was based on a histogram. I showed him how you can use the OUTHIST= option on the HISTOGRAM statement in PROC UNIVARIATE to obtain that information. For example, the following call to PROC UNIVARIATE creates a histogram for

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