STAT2

Chapter 0: What Is a Statistical Model?

• • 0.1 Model Basics
• • 0.2 A Four-Step Process

Chapter 1: Simple Linear Regression

• • 1.1 The Simple Linear Regression Model
• • 1.2 Conditions for a Simple Linear Model
• • 1.3 Assessing Conditions
• • 1.4 Transformations/Reexpressions
• • 1.5 Outliers and Influential Points

Chapter 2: Inference for Simple Linear Regression

• • 2.1 Inference for Regression Slope
• • 2.2 Partitioning Variability—ANOVA
• • 2.3 Regression and Correlation
• • 2.4 Intervals for Predictions
• • 2.5 Case Study: Butterfly Wings

Chapter 3: Multiple Regression

• • 3.1 Multiple Linear Regression Model
• • 3.2 Assessing a Multiple Regression Model
• • 3.3 Comparing Two Regression Lines
• • 3.4 New Predictors from Old
• • 3.5 Correlated Predictors
• • 3.6 Testing Subsets of Predictors
• • 3.7 Case Study: Predicting in Retail Clothing

Chapter 4: Additional Topics in Regression

• • 4.1 Topic: Added Variable Plots
• • 4.2 Topic: Techniques for Choosing Predictors
• • 4.3 Cross-validation
• • 4.4 Topic: Identifying Unusual Points in Regression
• • 4.5 Topic: Coding Categorical Predictors
• • 4.6 Topic: Randomization Test for a Relationship
• • 4.7 Topic: Bootstrap for Regression

Chapter 5: One-way ANOVA and Randomized Experiments

• • 5.1 Overview of ANOVA
• • 5.2 The One-way Randomized Experiment and Its Observational Sibling
• • 5.3 Fitting the Model
• • 5.4 Formal Inference: Assessing and Using the Model
• • 5.5 How Big Is the Effect?: Confidence Intervals and Effect Sizes
• • 5.6 Using Plots to Help Choose a Scale for the Response
• • 5.7 Multiple Comparisons and Fisher’s Least Significant Difference
• • 5.8 Case Study: Words with Friends

Chapter 6: Blocking and Two-way ANOVA

• • 6.1 Choose: RCB Design and Its Observational Relatives
• • 6.2 Exploring Data from Block Designs
• • 6.3 Fitting the Model for a Block Design
• • 6.4 Assessing the Model for a Block Design
• • 6.5 Using the Model for a Block Design

Chapter 7: ANOVA with Interaction and Factorial Designs

• • 7.1 Interaction
• • 7.2 Design: The Two-way Factorial Experiment
• • 7.3 Exploring Two-way Data
• • 7.4 Fitting a Two-way Balanced ANOVA Model
• • 7.5 Assessing Fit: Do We Need a Transformation?
• • 7.6 USING a Two-way ANOVA Model

Chapter 8: Additional Topics in Analysis of Variance

• • 8.1 Topic: Levene’s Test for Homogeneity of Variances
• • 8.2 Topic: Multiple Tests
• • 8.3 Topic: Comparisons and Contrasts
• • 8.4 Topic: Nonparametric Statistics
• • 8.5 Topic: Randomization F-Test
• • 8.6 Topic: Repeated Measures Designs and Data Sets
• • 8.7 Topic: ANOVA and Regression with Indicators
• • 8.8 Topic: Analysis of Covariance

Chapter 9: Logistic Regression

• • 9.1 Choosing a Logistic Regression Model
• • 9.2 Logistic Regression and Odds Ratios
• • 9.3 Assessing the Logistic Regression Model
• • 9.4 Formal Inference: Tests and Intervals

Chapter 10: Multiple Logistic Regression

• • 10.1 Overview
• • 10.2 Choosing, Fitting, and Interpreting Models
• • 10.3 Checking Conditions
• • 10.4 Formal Inference: Tests and Intervals
• • 10.5 Case study: Attractiveness and Fidelity

Chapter 11: Additional Topics in Logistic Regression

• • 11.1 Topic: Fitting the Logistic Regression Model
• • 11.2 Topic: Assessing Logistic Regression Models
• • 11.3 Randomization Tests for Logistic Regression
• • 11.4 Analyzing Two-Way Tables with Logistic Regression
• • 11.5 Simpson’s Paradox

Chapter 12: Time Series Analysis

• • 12.1 Functions of Time
• • 12.2 Measuring Dependence on Past Values: Autocorrelation
• • 12.3 ARIMA models
• • 12.4 Case Study: Residual Oil