The DO Loop
Statistical programming in SAS with an emphasis on SAS/IML programs![Summary statistics and t tests in SAS t test for difference between group means in SAS](https://blogs.sas.com/content/iml/files/2017/07/summarystats4-640x336.png)
Students in introductory statistics courses often use summary statistics (such as sample size, mean, and standard deviation) to test hypotheses and to compute confidence intervals. Did you know that you can provide summary statistics (rather than raw data) to PROC TTEST in SAS and obtain hypothesis tests and confidence intervals?
![The average bootstrap sample omits 36.8% of the data](https://blogs.sas.com/content/iml/files/2017/06/bootdup3-640x336.png)
Suppose you roll six identical six-sided dice. Chance are that you will see at least one repeated number. The probability that you will see six unique numbers is very small: only 6! / 6^6 ≈ 0.015. This example can be generalized. If you draw a random sample with replacement from
![Video: Create and use lists and tables in SAS/IML](https://blogs.sas.com/content/iml/files/2017/06/videoLists-702x336.png)
My presentation at SAS Global Forum 2017 was "More Than Matrices: SAS/IML Software Supports New Data Structures." The paper was published in the conference proceedings several months ago, but I recently recorded a short video that gives an overview of using the new data structures in SAS/IML 14.2: If your
![Jackknife estimates in SAS Jackknife](https://blogs.sas.com/content/iml/files/2017/06/jackknife-640x336.jpg)
One way to assess the precision of a statistic (a point estimate) is to compute the standard error, which is the standard deviation of the statistic's sampling distribution. A relatively large standard error indicates that the point estimate should be viewed with skepticism, either because the sample size is small
![How to find a feasible point for a constrained optimization in SAS](https://blogs.sas.com/content/iml/files/2017/06/nlpfea-500x336.png)
Most numerical optimization routines require that the user provides an initial guess for the solution. I have previously described a method for choosing an initial guess for an optimization, which works well for low-dimensional optimization problems. Recently a SAS programmer asked how to find an initial guess when there are
![Two ways to compute maximum likelihood estimates in SAS](https://blogs.sas.com/content/iml/files/2017/06/loglikcreate2-640x336.png)
In a previous article, I showed two ways to define a log-likelihood function in SAS. This article shows two ways to compute maximum likelihood estimates (MLEs) in SAS: the nonlinear optimization subroutines in SAS/IML and the NLMIXED procedure in SAS/STAT. To illustrate these methods, I will use the same data