The DO Loop

Analytics

Find out how analytics, from data mining to cognitive computing, is changing the way we do business

Rick WicklinApril 24, 2019 0

The CUSUM test has many incarnations. Different areas of statistics use different assumption and test for different hypotheses. This article presents a brief overview of CUSUM tests and gives an example of using the CUSUM test in PROC AUTOREG for autoregressive models in SAS. A CUSUM test uses the cumulative

English

Analytics | Programming Tips

Rick WicklinApril 10, 2019 3

4 ways to compute an SSCP matrix

In numerical linear algebra, there are often multiple ways to solve a problem, and each way is useful in various contexts. In fact, one of the challenges in matrix computations is choosing from among different algorithms, which often vary in their use of memory, data access, and speed. This article

English

Analytics | Data Visualization

Rick WicklinMarch 27, 2019 2

How to simulate multivariate outliers

In simulation studies, sometimes you need to simulate outliers. For example, in a simulation study of regression techniques, you might want to generate outliers in the explanatory variables to see how the technique handles high-leverage points. This article shows how to generate outliers in multivariate normal data that are a

English

Analytics | Data Visualization

Rick WicklinMarch 4, 2019 1

Visualize SAT scores in North Carolina

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

English

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

English

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

English

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

English

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

English

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

English

Blogs

Blogs

Analytics