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

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.

This post demonstrates how to set up and use the Autoexec File when you start a SAS session.

Using a standard header in your SAS programs is one of the best habits to get under your skin in order to allow other SAS users to quickly get an overview of your code

Missing values are part of the game when you are dealing with data. This post shows you two different ways of replacing missing values with zero in SAS.

In my college days studying mathematical finance, derivative pricing theory was among the hottest of topics. The first model that is usually taught in these classes is the famous Black-Scholes option pricing model. This post shows different ways of computing Black-Scholes prices 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.

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.

Dates are everywhere in data science. Whether you are doing statistical analysis, data manipulation or something else in SAS, you will often have to work with date values. Therefore it is important to understand how SAS interprets dates and how to manipulate them correctly.

Most SAS users are familiar with the data step LAG function to look back on previous observations. Since the data step does not support a LEAD function, fewer SAS users are familiar with looking ahead in a dataset. This post presents different approaches of doing so, some more efficient than others.