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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 not-so-simple, 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

1. About This Book
2. About These Authors
3. Preface
4. Chapter 1: Statistics and an Introduction to the SAS University Edition
1. 1.1 Introduction
2. 1.2 Measurements and Observations
3. 1.3 Nominal or Categorical Measurements
4. 1.4 Ordinal Scale Measurements
5. 1.5 Interval Scales
6. 1.6 Ratio Scales
7. 1.7 Populations and Samples
8. 1.8 SAS University Edition
5. Chapter 2: Data Description and Simple Inference
1. 2.1 Introduction
2. 2.2 Summary Statistics and Graphical Representations of Data
3. 2.3 Testing Hypotheses and Student’s t-Test
4. 2.4 The t-Test for Paired Data
5. 2.5 Exercises
6. Chapter 3: Categorical Data
1. 3.1 Introduction
2. 3.2 Graphing and Analysing Frequencies: Horse Race Winners
3. 3.3 Two-By-Two Tables
4. 3.4 Larger Cross-Tabulations
5. 3.5 Fisher’s Exact Test
6. 3.6 McNemar’s Test Example
7. 3.7 Exercises
7. Chapter 4: Bivariate Data: Scatterplots, Correlation, and Regression
1. 4.1 Introduction
2. 4.2 Scatter Plots, the Correlation Coefficient, and Simple Linear Regression
3. 4.3 Adjusting the Scatter Plot To Show Patterns in the Data
4. 4.4 Exercises
8. Chapter 5: Analysis of Variance
1. 5.1 Introduction
2. 5.2 One-Way ANOVA
3. 5.3 Two-Way ANOVA
4. 5.4 Unbalanced ANOVA
5. 5.5 Exercises
9. Chapter 6: Multiple Linear Regression
1. 6.1 Introduction
2. 6.2 Multiple Linear Regression
3. 6.3 Identifying a Parsimonious Regression Model
4. 6.4 Exercises
10. Chapter 7: Logistic Regression
1. 7.1 Introduction
2. 7.2 Logistic Regression
3. 7.3 Logistic Regression for 1:1 Matched Studies
4. 7.4 Summary
5. 7.5 Exercises
11. Chapter 8: Poisson Regression and the Generalized Linear Model
1. 8.1 Introduction
2. 8.2 Generalized Linear Model
3. 8.3 Poisson Regression
4. 8.4 Overdispersion
5. 8.5 Summary
6. 8.6 Exercises
12. References
13. Index