book

Essential Statistics Using SAS University Edition

Name: Essential Statistics Using SAS University Edition
ISBN: 9781629600949

by Geoff Der, Brian S. Everitt

December 2015

Beginner

246 pages

5h 8m

English

SAS Institute

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About This Book
About These Authors
Preface
Chapter 1: Statistics and an Introduction to the SAS University Edition
1.1 Introduction1.2 Measurements and Observations1.3 Nominal or Categorical Measurements1.4 Ordinal Scale Measurements1.5 Interval Scales1.6 Ratio Scales1.7 Populations and Samples1.8 SAS University Edition1.8.1 Starting SAS University Edition1.8.2 The SAS Studio Interface1.8.3 The Navigation Pane1.8.4 The Work Area1.8.5 Tasks and Task Settings1.8.6 The Data Tab1.8.7 The Model Tab1.8.8 The Options Tab1.8.9 The Output Tab1.8.10 The Information Tab1.8.11 Abbreviations of Task Settings Used in This Book1.8.12 The Results Pane1.8.13 Options and Preferences1.8.14 Setting Up the Data Used in This Book
Chapter 2: Data Description and Simple Inference
2.1 Introduction2.2 Summary Statistics and Graphical Representations of Data2.2.1 Initial Analysis of Room Width Guesses Using Simple Summary Statistics and Graphics2.3 Testing Hypotheses and Student’s t-Test2.3.1 Applying Student’s t-Test to the Guesses of Room Width2.3.2 Checking the Assumptions Made When Using Student’s t-Test and Alternatives to the t-Test2.4 The t-Test for Paired Data2.4.1 Initial Analysis of Wave Energy Data Using Box Plots2.4.2 Wave Power and Mooring Methods: Do Two Mooring Methods Differ in Bending Stress?2.4.3 Checking the Assumptions of the Paired t-Tests2.5 Exercises
Chapter 3: Categorical Data
3.1 Introduction3.2 Graphing and Analysing Frequencies: Horse Race Winners3.2.1 Looking at Horse Race Winners Using Some Simple Graphics: Bar Charts and Pie Charts3.2.2 Chi-Square Goodness-of-Fit Test: Does Starting Stall Position Predict Horse Race Winners?3.3 Two-By-Two Tables3.3.1 Chi-Square Test: Breast Self-Examination3.3.2 Odds and Odds Ratios3.3.3 The Cochran-Mantel-Haenszel Test for Multiple Related 2 X 2 Tables3.4 Larger Cross-Tabulations3.4.1 Tabulating the Brain Tumour Data Into a Contingency Table3.4.2 Do Different Types Of Brain Tumours Occur More Frequently at Particular Sites? Chi-Squared Test3.5 Fisher’s Exact Test3.5.1 How Is Baiting Behaviour at Suicides Affected by Season? Fisher’s Exact Test3.5.2 Fisher’s Exact Test for Larger Tables3.6 McNemar’s Test Example3.6.1 Juvenile Felons: Where Should They Be Tried? McNemar’s Test3.7 Exercises
Chapter 4: Bivariate Data: Scatterplots, Correlation, and Regression
4.1 Introduction4.2 Scatter Plots, the Correlation Coefficient, and Simple Linear Regression4.2.1 The Scatter Plot4.2.2 The Correlation Coefficient4.2.3 Simple Linear Regression4.2.4 A Further Example of Linear Regression4.2.5 Checking Model Assumptions: The Use of Residuals4.3 Adjusting the Scatter Plot To Show Patterns in the Data4.3.1 Plotting the Birthrate Data: The Aspect Ratio of a Scatter Plot4.4 Exercises
Chapter 5: Analysis of Variance
5.1 Introduction5.2 One-Way ANOVA5.2.1 Initial Examination of Teaching Arithmetic Data with Summary Statistics and Box Plots5.2.2 Teaching Arithmetic: Are Some Methods for Teaching Arithmetic Better than Others?5.3 Two-Way ANOVA5.3.1 A First Look at the Rat Weight Gain Data Using Box Plots and Numerical Summaries5.3.2 Weight Gain in Rats: Do Rats Gain More Weight on a Particular Diet?5.4 Unbalanced ANOVA5.4.1 Summarizing the Post-Natal Depression Data5.4.2 Post-Natal Depression: Is a Child’s IQ Affected?5.5 Exercises
Chapter 6: Multiple Linear Regression
6.1 Introduction6.2 Multiple Linear Regression6.2.1 The Ice Cream Data: An Initial Analysis Using Scatter Plots6.2.2 Ice Cream Sales: Are They Most Affected by Price or Temperature? How to Tell Using Multiple Regression6.2.3 Diagnosing the Multiple Regression Model Fitted to the Ice Cream Consumption Data: The Use of Residuals6.2.4 A More Complex Example of the Use of Multiple Linear Regression6.2.5 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 Terms6.3 Identifying a Parsimonious Regression Model6.4 Exercises
Chapter 7: Logistic Regression
7.1 Introduction7.2 Logistic Regression7.2.1 Intra-Abdominal Sepsis: Using Logistic Regression to Answer the Question of What Predicts Survival after Surgery7.2.2 Odds7.2.3 Applying the Logistic Regression Model with a Single Explanatory Variable7.2.4 Logistic Regression with All the Explanatory Variables7.2.5 A Second Example of the Use of Logistic Regression7.2.6 An Initial Look at the Caseness Data7.2.7 Modeling the Caseness Data Using Logistic Regression7.3 Logistic Regression for 1:1 Matched Studies7.4 Summary7.5 Exercises

Chapter 8: Poisson Regression and the Generalized Linear Model
8.1 Introduction8.2 Generalized Linear Model8.2.1 Components of Generalized Linear Models8.2.2 Using the Generalized Linear Model to Apply Multiple Linear Regression and Logistic Regression8.3 Poisson Regression8.3.1 Example 1: Familial Adenomatous Polyposis (FAP)8.3.2 Example 2: Bladder Cancer8.4 Overdispersion8.5 Summary8.6 Exercises
References
Index

Overview

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.

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