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# Solution Manual for STAT2, 2nd Edition, Ann Cannon, George W. Cobb, Bradley A. Hartlaub, Julie M. Legler, Robin H. Lock, Thomas L. Moore, Allan J. Rossman Jeffrey A. Witmer, ISBN: 9781319209513, ISBN: 9781319067502, ISBN: 9781319056971, ISBN: 9781319054076

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Solution Manual for STAT2, 2nd Edition, Ann Cannon, George W. Cobb, Bradley A. Hartlaub, Julie M. Legler, Robin H. Lock, Thomas L. Moore, Allan J. Rossman Jeffrey A. Witmer, ISBN: 9781319209513, ISBN: 9781319067502, ISBN: 9781319056971, ISBN: 9781319054076

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**Table of Contents**

Chapter 0 What Is a Statistical Model?

0.1 Model Basics

0.2 A Four-Step Process

Unit A: Linear Regression

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

Unit B: Analysis of Variance

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

Unit C: Logistic Regression

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

Answers to Selected Exercises

General Index

Dataset Index

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