The DO Loop
Statistical programming in SAS with an emphasis on SAS/IML programs![Use the EFFECTPLOT statement to visualize regression models in SAS](https://blogs.sas.com/content/iml/files/2016/06/effectplot2.png)
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
![The SELECT statement in the SAS DATA step Flow chart diagram for a switch statement (SELECT statement)](https://blogs.sas.com/content/iml/files/2016/06/selectwhen.png)
Every beginning SAS programmer learns the simple IF-THEN/ELSE statement for conditional processing in the SAS DATA step. The basic If-THEN statement handles two cases: if a condition is true, the program does one thing, otherwise the program does something else. Of course, you can handle more cases by using multiple
![Grids and linear subspaces](https://blogs.sas.com/content/iml/files/2016/05/span2d-e1464091064367.png)
A grid is a set of evenly spaced points. You can use SAS to create a grid of points on an interval, in a rectangular region in the plane, or even in higher-dimensional regions like the parallelepiped shown at the left, which is generated by three vectors. You can use
![How to fit a variety of logistic regression models in 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](https://blogs.sas.com/content/iml/files/2014/06/LogisticSim.png)
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
![Create a package in SAS/IML](https://blogs.sas.com/content/iml/files/2016/05/package3.png)
In a previous post I showed how to download, install, and use packages in SAS/IML 14.1. SAS/IML packages incorporate source files, documentation, data sets, and sample programs into a ZIP file. The PACKAGE statement enables you to install, uninstall, and manage packages. You can load functions and data into your
![Save descriptive statistics for multiple variables in a SAS data set](https://blogs.sas.com/content/iml/files/2016/03/descstatuni1.png)
Descriptive univariate statistics are the foundation of data analysis. Before you create a statistical model for new data, you should examine descriptive univariate statistics such as the mean, standard deviation, quantiles, and the number of nonmissing observations. In SAS, there is an easy way to create a data set that