SAS Certification Prep Guide

Table of contents

1. About this Book
2. Chapter 1: Statistics and Making Sense of Our World
   1. Introduction
   2. What Is Statistics?
   3. Variable Types and SAS Data Types
   4. The Data Analytics Process
   5. Getting Started with SAS
   6. Key Terms
3. Chapter 2: Summarizing Your Data with Descriptive Statistics
   1. Introduction
   2. Measures of Center
   3. Measures of Variation
   4. Measures of Shape
   5. Other Descriptive Measures
   6. The MEANS Procedure
   7. Key Terms
   8. Chapter Quiz
4. Chapter 3: Data Visualization
   1. Introduction
   2. View and Interpret Categorical Data
   3. View and Interpret Numeric Data
   4. Visual Analyses Using the SGPLOT Procedure
   5. Key Terms
   6. Chapter Quiz
5. Chapter 4: The Normal Distribution and Introduction to Inferential Statistics
   1. Introduction
   2. Continuous Random Variables
   3. The Sampling Distribution of the Mean
   4. Introduction to Hypothesis Testing
   5. Hypothesis Testing for the Population Mean (σ Known)
   6. Hypothesis Testing for the Population Mean (σ Unknown)
   7. Key Terms
   8. Chapter Quiz
6. Chapter 5: Analysis of Categorical Variables
   1. Introduction
   2. Testing the Independence of Two Categorical Variables
   3. Measuring the Strength of Association between Two Categorical Variables
   4. Key Terms
   5. Chapter Quiz
7. Chapter 6: Two-Sample t-Test
   1. Introduction
   2. Independent Samples
   3. Paired Samples
   4. Key Terms
   5. Chapter Quiz
8. Chapter 7: Analysis of Variance (ANOVA)
   1. Introduction
   2. One-Factor Analysis of Variance
   3. The Randomized Block Design
   4. Two-Factor Analysis of Variance
   5. Key Terms
   6. Chapter Quiz
9. Chapter 8: Preparing the Input Variables for Prediction
   1. Introduction
   2. Missing Values
   3. Categorical Input Variables
   4. Variable Clustering
   5. Variable Screening
   6. Key Terms
   7. Chapter Quiz
10. Chapter 9: Linear Regression Analysis
    1. Introduction
    2. Exploring the Relationship between Two Continuous Variables
    3. Simple Linear Regression
    4. Multiple Linear Regression
    5. Variable Selection Using the REG and GLMSELECT Procedures
    6. Assessing the Validity of Results Using Regression Diagnostics
    7. Concluding Remarks
    8. Key Terms
    9. Chapter Quiz
11. Chapter 10: Logistic Regression Analysis
    1. Introduction
    2. The Logistic Regression Model
    3. Logistic Regression with a Categorical Predictor
    4. The Multiple Logistic Regression Model
    5. Scoring New Data
    6. Key Terms
    7. Chapter Quiz
12. Chapter 11: Measure of Model Performance
    1. Introduction
    2. Preparation for the Modeling Phase
    3. Assessing Classifier Performance
    4. Adjustment to Performance Estimates When Oversampling Rare Events
    5. The Use of Decision Theory for Model Selection
    6. Key Terms
    7. Chapter Quiz
13. References