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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

Name: 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
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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,

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This is completed downloadable of 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

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ISBN-10 ‏ : ‎ 1319054072
ISBN-13 ‏ : ‎ 978-1319054076
Author: Ann Cannon, George W. Cobb, Bradley A. Hartlaub, Julie M. Legler, Robin H. Lock, Thomas L. Moore, Allan J. Rossman Jeffrey A. Witmer

STAT2 introduces students to statistical modeling beyond what they have learned in a Stat 101 college course or an AP Statistics course. Building on basic concepts and methods learned in that course, STAT2 empowers students to analyze richer datasets that include more variables and address a broader range of research questions.

Other than a working understanding of exponential and logarithmic functions, there are no prerequisites beyond successful completion of their first statistics course. To help all students make a smooth transition to this course, Chapter 0 reminds students of basic statistical terminology and also uses the familiar two-sample t-test as a way to illustrate the approach of specifying, estimating, and testing a statistical model.

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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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

This is completed downloadable of 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

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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

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