You are previewing Essential Statistics Using SAS University Edition.
O'Reilly logo
Essential Statistics Using SAS University Edition

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 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
      1. 1.8.1 Starting the SAS University Edition
      2. 1.8.2 The SAS University Edition User Interface
      3. 1.8.3 The Navigation Pane
      4. 1.8.4 The Work Area
      5. 1.8.5 Tasks and Task Settings
      6. 1.8.6 The Data Tab
      7. 1.8.7 The Model Tab
      8. 1.8.8 The Options Tab
      9. 1.8.9 The Output Tab
      10. 1.8.10 The Information Tab
      11. 1.8.11 Abbreviations of Task Settings Used in This Book
      12. 1.8.12 The Results Pane
      13. 1.8.13 Options and Preferences
      14. 1.8.14 Setting Up the Data Used in This Book
  5. Chapter 2: Data Description and Simple Inference
    1. 2.1 Introduction
    2. 2.2 Summary Statistics and Graphical Representations of Data
      1. 2.2.1 Initial Analysis of Room Width Guesses Using Simple Summary Statistics and Graphics
    3. 2.3 Testing Hypotheses and Student’s t-Test
      1. 2.3.1 Applying Student’s t-Test to the Guesses of Room Width
      2. 2.3.2 Checking the Assumptions Made When Using Student’s t-Test and Alternatives to the t-Test
    4. 2.4 The t-Test for Paired Data
      1. 2.4.1 Initial Analysis of Wave Energy Data Using Box Plots
      2. 2.4.2 Wave Power and Mooring Methods: Do Two Mooring Methods Differ in Bending Stress?
      3. 2.4.3 Checking the Assumptions of the Paired t-Tests
    5. 2.5 Exercises
  6. Chapter 3: Categorical Data
    1. 3.1 Introduction
    2. 3.2 Graphing and Analysing Frequencies: Horse Race Winners
      1. 3.2.1 Looking at Horse Race Winners Using Some Simple Graphics: Bar Charts and Pie Charts
      2. 3.2.2 Chi-Square Goodness-of-Fit Test: Does Starting Stall Position Predict Horse Race Winners?
    3. 3.3 Two-By-Two Tables
      1. 3.3.1 Chi-Square Test: Breast Self-Examination
      2. 3.3.2 Odds and Odds Ratios
      3. 3.3.3 The Cochrane-Mantel-Haenszel Test for Multiple Related 2 X 2 Tables
    4. 3.4 Larger Cross-Tabulations
      1. 3.4.1 Tabulating the Brain Tumour Data Into a Contingency Table
      2. 3.4.2 Do Different Types Of Brain Tumours Occur More Frequently at Particular Sites? Chi-Squared Test
    5. 3.5 Fisher’s Exact Test
      1. 3.5.1 How Is Baiting Behaviour at Suicides Affected by Season? Fisher’s Exact Test
      2. 3.5.2 Fisher’s Exact Test for Larger Tables
    6. 3.6 McNemar’s Test Example
      1. 3.6.1 Juvenile Felons: Where Should They Be Tried? McNemar’s Test
    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
      1. 4.2.1 The Scatter Plot
      2. 4.2.2 The Correlation Coefficient
      3. 4.2.3 Simple Linear Regression
      4. 4.2.4 A Further Example of Linear Regression
      5. 4.2.5 Checking Model Assumptions: The Use of Residuals
    3. 4.3 Adjusting the Scatter Plot To Show Patterns in the Data
      1. 4.3.1 Plotting the Birthrate Data: The Aspect Ratio of a Scatter Plot
    4. 4.4 Exercises
  8. Chapter 5: Analysis of Variance
    1. 5.1 Introduction
    2. 5.2 One-Way ANOVA
      1. 5.2.1 Initial Examination of Teaching Arithmetic Data with Summary Statistics and Box Plots
      2. 5.2.2 Teaching Arithmetic: Are Some Methods for Teaching Arithmetic Better than Others?
    3. 5.3 Two-Way ANOVA
      1. 5.3.1 A First Look at the Rat Weight Gain Data Using Box Plots and Numerical Summaries
      2. 5.3.2 Weight Gain in Rats: Do Rats Gain More Weight on a Particular Diet?
    4. 5.4 Unbalanced ANOVA
      1. 5.4.1 Summarizing the Post-Natal Depression Data
      2. 5.4.2 Post-Natal Depression: Is a Child’s IQ Affected?
    5. 5.5 Exercises
  9. Chapter 6: Multiple Linear Regression
    1. 6.1 Introduction
    2. 6.2 Multiple Linear Regression
      1. 6.2.1 The Ice Cream Data: An Initial Analysis Using Scatter Plots
      2. 6.2.2 Ice Cream Sales: Are They Most Affected by Price or Temperature? How to Tell Using Multiple Regression
      3. 6.2.3 Diagnosing the Multiple Regression Model Fitted to the Ice Cream Consumption Data: The Use of Residuals
      4. 6.2.4 A More Complex Example of the Use of Multiple Linear Regression
      5. 6.2.5 The Cloud Seeding Data: Initial Examination of the Data Using Box Plots and Scatter Plots.
      6. 6.2.6 When Is Cloud Seeding Best Carried Out? How to Tell Using Multiple Regression Models Containing Interaction Terms
    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
      1. 7.2.1 Intra-Abdominal Sepsis: Using Logistic Regression to Answer the Question of What Predicts Survival after Surgery
      2. 7.2.2 Odds
      3. 7.2.3 Applying the Logistic Regression Model with a Single Explanatory Variable
      4. 7.2.4 Logistic Regression with All the Explanatory Variables
      5. 7.2.5 A Second Example of the Use of Logistic Regression
      6. 7.2.6 An Initial Look at the Caseness Data
      7. 7.2.7 Modeling the Caseness Data Using 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
      1. 8.2.1 Components of Generalized Linear Models
      2. 8.2.2 Using the Generalized Linear Model to Apply Multiple Linear Regression and Logistic Regression
    3. 8.3 Poisson Regression
      1. 8.3.1 Example 1: Familial Adenomatous Polyposis (FAP)
      2. 8.3.2 Example 2: Bladder Cancer
    4. 8.4 Overdispersion
    5. 8.5 Summary
    6. 8.6 Exercises
  12. References
  13. Index