Three new books have made their way on to my SAS shelf. Being a SAS programmer means being on a never ending learning curve. The many great SAS books out there helps me stay on the curve.

Browsing Category

Three new books have made their way on to my SAS shelf. Being a SAS programmer means being on a never ending learning curve. The many great SAS books out there helps me stay on the curve.

Beginner Tips, Data Manipulation, Finance, Graph, Linear Modelling, ODS, SAS/IML, Simulation, Statistics, Time Series,

Have you ever been stuck on a SAS problem and not even Google seems to be of help? Fear not! Help is on the way in the SAS online communities.

Beginner Tips, Data Manipulation, Finance, Graph, Linear Modelling, ODS, SAS/IML, Simulation, Statistics, Time Series,

Learning SAS programming, I like to learn new stuff and to learn more about the subjects I am already familiar with. SAS books is a great way to do this. Here are the SAS books on my shelf.

Using SAS Procedures to calculate statistical sizes, you often need to save them for later analysis. CALL SYMPUTX does this efficiently.

Moving Averages are frequently used when dealing with time series data. This post shows examples of creating moving averages in SAS, using the Data Step, SAS/IML and PROC EXPAND

Fitting discrete distributions to univariate data in SAS requires more work than the continuous case. Here I present examples of fitting the Poisson and Negative Binomial Distribution.

The Central Limit Theorem is one of the most fundamental parts of statistics. Get a visual demonstration of the theorem here.

The amount of output from even small SAS procedures can be overwhelming. Take control of your output with ODS trace and ODS Select/Exclude Statements.

Knowing your data well is one of the first steps in many statistical applications. Assessing the distribution of single variables is one way of getting to know your data. This post presents an example of doing so with PROC UNIVARIATE.

Linear regression is one of the simplest statistical models and is easily fitted in SAS. In this post I present a short explanation of the linear regression model and different ways to fit the model with SAS/STAT procedures and the IML language.