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Rick WicklinMarch 4, 2019 1

Standardized tests like the SAT and ACT can cause stress for both high school students and their parents, but according to a Wall Street Journal article, the SAT and ACT "provide an invaluable measure of how students are likely to perform in college and beyond." Naturally, students wonder how their

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Analytics | Data Visualization | Learn SAS

Rick WicklinFebruary 25, 2019 0

Graphs of bootstrap statistics in PROC TTEST

When I run a bootstrap analysis, I create graphs to visualize the distribution of the bootstrap statistics. For example, in my article about how to bootstrap the difference of means in a two-sample t test, I included a histogram of the bootstrap distribution and added reference lines to indicate a

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Analytics | Data Visualization

Rick WicklinFebruary 20, 2019 7

An easier way to create a calibration plot in SAS

Last year I published a series of blogs posts about how to create a calibration plot in SAS. A calibration plot is a way to assess the goodness of fit for a logistic model. It is a diagnostic graph that enables you to qualitatively compare a model's predicted probability of

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Analytics | Learn SAS | Programming Tips

Rick WicklinFebruary 13, 2019 0

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

Analytics | Learn SAS

Rick WicklinFebruary 11, 2019 13

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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Analytics | Programming Tips

Rick WicklinJanuary 23, 2019 0

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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Analytics | Machine Learning | Programming Tips

Partition data into training, validation, and testing in SAS

Rick WicklinJanuary 21, 2019 7

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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Analytics | Programming Tips

Rick WicklinJanuary 16, 2019 10

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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Analytics | Data Visualization | Programming Tips

Process flow diagram shows how to resample data to create a bootstrap distribution.

Rick WicklinJanuary 9, 2019 0

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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Analytics | Programming Tips

Rick WicklinJanuary 7, 2019 36

Deming regression for comparing different measurement methods

Deming regression (also called errors-in-variables regression) is a total regression method that fits a regression line when the measurements of both the explanatory variable (X) and the response variable (Y) are assumed to be subject to normally distributed errors. Recall that in ordinary least squares regression, the explanatory variable (X)

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