Book Description
Students and instructors of statistics courses using SAS University Edition will welcome this book. Learning fundamental statistics is essential to solving problems with SAS. Essential Statistics Using SAS University Edition demonstrates how to use SAS University Edition to apply a variety of statistical methodologies, from the simple to the notsosimple, to a range of data sets. Learn how to apply the appropriate statistical method to answer a particular question about a data set, and correctly interpret the numerical results that you obtain. SAS University Edition users who are new to SAS or who need a refresher course will benefit from the statistics overview and topics, such as multiple linear regression, logistic regression, and Poisson regression.Table of Contents
 About This Book
 About These Authors
 Preface

Chapter 1: Statistics and an Introduction to the SAS University Edition
 1.1 Introduction
 1.2 Measurements and Observations
 1.3 Nominal or Categorical Measurements
 1.4 Ordinal Scale Measurements
 1.5 Interval Scales
 1.6 Ratio Scales
 1.7 Populations and Samples

1.8 SAS University Edition
 1.8.1 Starting the SAS University Edition
 1.8.2 The SAS University Edition User Interface
 1.8.3 The Navigation Pane
 1.8.4 The Work Area
 1.8.5 Tasks and Task Settings
 1.8.6 The Data Tab
 1.8.7 The Model Tab
 1.8.8 The Options Tab
 1.8.9 The Output Tab
 1.8.10 The Information Tab
 1.8.11 Abbreviations of Task Settings Used in This Book
 1.8.12 The Results Pane
 1.8.13 Options and Preferences
 1.8.14 Setting Up the Data Used in This Book
 Chapter 2: Data Description and Simple Inference
 Chapter 3: Categorical Data
 Chapter 4: Bivariate Data: Scatterplots, Correlation, and Regression
 Chapter 5: Analysis of Variance

Chapter 6: Multiple Linear Regression
 6.1 Introduction

6.2 Multiple Linear Regression
 6.2.1 The Ice Cream Data: An Initial Analysis Using Scatter Plots
 6.2.2 Ice Cream Sales: Are They Most Affected by Price or Temperature? How to Tell Using Multiple Regression
 6.2.3 Diagnosing the Multiple Regression Model Fitted to the Ice Cream Consumption Data: The Use of Residuals
 6.2.4 A More Complex Example of the Use of Multiple Linear Regression
 6.2.5 The Cloud Seeding Data: Initial Examination of the Data Using Box Plots and Scatter Plots.
 6.2.6 When Is Cloud Seeding Best Carried Out? How to Tell Using Multiple Regression Models Containing Interaction Terms
 6.3 Identifying a Parsimonious Regression Model
 6.4 Exercises

Chapter 7: Logistic Regression
 7.1 Introduction

7.2 Logistic Regression
 7.2.1 IntraAbdominal Sepsis: Using Logistic Regression to Answer the Question of What Predicts Survival after Surgery
 7.2.2 Odds
 7.2.3 Applying the Logistic Regression Model with a Single Explanatory Variable
 7.2.4 Logistic Regression with All the Explanatory Variables
 7.2.5 A Second Example of the Use of Logistic Regression
 7.2.6 An Initial Look at the Caseness Data
 7.2.7 Modeling the Caseness Data Using Logistic Regression
 7.3 Logistic Regression for 1:1 Matched Studies
 7.4 Summary
 7.5 Exercises
 Chapter 8: Poisson Regression and the Generalized Linear Model
 References
 Index
Product Information
 Title: Essential Statistics Using SAS University Edition
 Author(s):
 Release date: December 2015
 Publisher(s): SAS Institute
 ISBN: 9781629600949