Biostatistics by Example Using SAS Studio

Learn how to solve basic statistical problems with Ron Cody’s easy-to-follow style using the point-and-click SAS Studio tasks.

Aimed specifically at the health sciences, Biostatistics by Example Using SAS Studio, provides an introduction to SAS Studio tasks. The book includes many biological and health-related problem sets and is fully compatible with SAS University Edition.

After reading this book you will be able to understand temporary and permanent SAS data sets, and you will learn how to create them from various data sources. You will also be able to use SAS Studio statistics tasks to generate descriptive statistics for continuous and categorical data.
The inferential statistics portion of the book covers the following topics:

• paired and unpaired t tests
• one-way analysis of variance
• N-way ANOVA
• correlation
• simple and multiple regression
• logistic regression
• categorical data analysis
• power and sample size calculations

Besides describing each of these statistical tests, the book also discusses the assumptions that need to be met before running and interpreting these tests. For two-sample tests and N-way tests, nonparametric tests are also described.

This book leads you step-by-step through each of the statistical tests with numerous screen shots, and you will see how to read and interpret all of the output generated by these tests.

Experience with some basic statistical tests used to analyze medical data or classroom experience in biostatistics or statistics is required. Although the examples are related to the medical and biology fields, researchers in other fields such as psychology or education will find this book helpful. No programming experience is required.

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Table of Contents

Chapter 1: What Is the SAS University Edition?
Chapter 2: SAS Studio Tasks
Chapter 3: Importing Data into SAS
Chapter 4: Reading Data from Text Files
Chapter 5: Descriptive Statistics – Univariate Analysis
Chapter 6: One-Sample Tests
Chapter 7: Two-Sample Tests
Chapter 8: Comparing More Than Two Means (ANOVA)
Chapter 9: N-Way ANOVA
Chapter 10: Correlation
Chapter 11: Simple and Multiple Regression
Chapter 12: Binary Logistic Regression
Chapter 13: Analyzing Categorical Data
Chapter 14: Computing Power and Sample Size