20
Oct

Compatibility between model data and the target population

To make accurate predictions, it is necessary that the sample data you use for model development is compatible with the target population. The distribution of each input used in the model should be similar in the sample and the target population. In your model you should include only those variables [...]

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9
Oct

An introduction to SAS Visual Analytics: the Parallel Period function of the Derived Item calculations

My new SAS Press book “An Introduction to SAS Visual Analytics” (written in collaboration with Tricia Aanderud and Rob Collum) covers all of the different aspects of SAS® Visual Analytics, including how to develop reports, load data, and handle administration. Below is an example of the types of tips that you can find [...]

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10
Aug

3 steps to prepare your data for accurate predictive models in SAS Enterprise Miner

In this post I describe the important tasks of data preparation, exploration and binning.These three steps enable you to know your data well and build accurate predictive models. First you need to clean your data. Cleaning includes eliminating variables which have uneven spread across the target variable. I give an [...]

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18
Jul

Organize your work with SAS® Enterprise Guide® Projects

Nowadays, whether you write SAS programs or use point-and-click methods to get results, you have choices for how you access SAS. Currently, when you open Base SAS most people get the traditional SAS windowing environment (aka Display Manager) as their interface. But it doesn’t have to be that way. If [...]

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17
Jul

How severe is your missing data problem?

Datasets are rarely ready for analysis, and one of the most prevalent problems is missing data. This post is the first in a short series focusing on how to think about missingness, how JMP13 can help us determine the scope of missing data in a given table, and how to [...]

The post How severe is your missing data problem? appeared first on SAS Learning Post.

27
Jun

How to perform an operation on all numeric or all character variables in a SAS data set

In a previous blog, I demonstrated a program and macro that could identify all numeric variables set to a specific value, such as 999. This blog discusses an immensely useful technique that allows you to perform an operation on all numeric or all character variables in a SAS data set. [...]

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26
Jun

Keeping your data set clean: Integrity constraints

When I teach my Data Cleaning course, the last topic I cover in the two-day course is SAS Integrity Constraints.  I find that most of the students, who are usually quite advanced programmers, have never heard of Integrity Constraints (abbreviated ICs).  I decided a short discussion on this topic would [...]

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6
Jun

Using regular expressions to verify the pattern of character data

Wait!  Don't close this window.  I understand that regular expressions can be very complicated (yes, there are many books on the subject), but some basic expressions to test patterns such as zip codes or telephone numbers are not that difficult.  In addition, you can sometimes use Google to search for [...]

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26
May

Standardizing phone numbers using SAS

How many times have you entered a phone number on a web page, only to be told that you did not type it the "correct" form? I find that annoying. Don't you? In my latest book, Cody's Data Cleaning Techniques, 3rd edition, I show how to convert a phone number [...]

The post Standardizing phone numbers using SAS appeared first on SAS Learning Post.

15
May

The Little SAS Program’s Big Night Out

The little SAS program’s official name was Extract_Transform_Load_ 0314.sas.  But, that name was much too formal, way too long, and did not roll off of the tongue very easily at all.  So, everybody simply called her:  ETL Pi. ETL Pi was conceived in a 2-hour project strategy meeting in conference [...]

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