Book Image

Hands-On SAS for Data Analysis

By : Harish Gulati
Book Image

Hands-On SAS for Data Analysis

By: Harish Gulati

Overview of this book

SAS is one of the leading enterprise tools in the world today when it comes to data management and analysis. It enables the fast and easy processing of data and helps you gain valuable business insights for effective decision-making. This book will serve as a comprehensive guide that will prepare you for the SAS certification exam. After a quick overview of the SAS architecture and components, the book will take you through the different approaches to importing and reading data from different sources using SAS. You will then cover SAS Base and 4GL, understanding data management and analysis, along with exploring SAS functions for data manipulation and transformation. Next, you'll discover SQL procedures and get up to speed on creating and validating queries. In the concluding chapters, you'll learn all about data visualization, right from creating bar charts and sample geographic maps through to assigning patterns and formats. In addition to this, the book will focus on macro programming and its advanced aspects. By the end of this book, you will be well versed in SAS programming and have the skills you need to easily handle and manage your data-related problems in SAS.
Table of Contents (17 chapters)
Free Chapter
1
Section 1: SAS Basics
4
Section 2: Merging, Optimizing, and Descriptive Statistics
7
Section 3: Advanced Programming
10
Section 4: SQL in SAS
13
Section 5: Data Visualization and Reporting

Histograms

The exact use of histograms is to assess the probability distribution of a given variable by plotting the frequencies of observations occurring in certain ranges of values. They were first described by Karl Pearson. In their most simplistic form, histograms plot the frequency of a variable in a range of values called bins. We have chosen to start this chapter by describing histograms as they are the simplest of graphs that only accommodate one variable. Adding a density curve makes them a bit more informative but let's start with the basic form of the histogram. We will use the Class dataset that has been extensively used in the previous chapters:

Proc SGPLOT Data = Class;
Histogram Height;
Title 'Height of children in class across years';
Run;
The only change to the Class dataset is that the Weight variable has been renamed Weights in this chapter.
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