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# Statistics

## Three New Additions to my SAS Shelf

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.

## Stuck on a SAS Problem? Ask for Help

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.

## What SAS Books Are On My Shelf?

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.

## Save Statistics in Macro Variables

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

## Moving Average in SAS

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

## Fit Discrete Distribution in SAS

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.

## Visualize the Central Limit Theorem in SAS

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

## Control your Output with ODS select/exclude

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.

## Fit Continuous Distribution in SAS

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 in SAS

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.