The DO Loop
Statistical programming in SAS with an emphasis on SAS/IML programs![Graphs of bootstrap statistics in PROC TTEST](https://blogs.sas.com/content/iml/files/2019/02/bootttestgraph1-640x336.png)
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
![An easier way to create a calibration plot in SAS](https://blogs.sas.com/content/iml/files/2019/02/CalibrationPlotLogi1-600x336.png)
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
![An easier way to perform regression with restricted cubic splines in SAS](https://blogs.sas.com/content/iml/files/2019/02/percentilelist-640x336.png)
Maybe if we think and wish and hope and pray It might come true. Oh, wouldn't it be nice? The Beach Boys Months ago, I wrote about how to use the EFFECT statement in SAS to perform regression with restricted cubic splines. This is the modern way to use splines
![3 ways to obtain the Hessian at the MLE solution for a regression model](https://blogs.sas.com/content/iml/files/2019/02/PLM6.png)
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
![4 reasons to use PROC PLM for linear regression models in SAS](https://blogs.sas.com/content/iml/files/2019/02/PLM2-640x336.png)
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
![Feature generation and correlations among features in machine learning](https://blogs.sas.com/content/iml/files/2019/02/effectCorr2-647x336.png)
Feature generation (also known as feature creation) is the process of creating new features to use for training machine learning models. This article focuses on regression models. The new features (which statisticians call variables) are typically nonlinear transformations of existing variables or combinations of two or more existing variables. This