### What's included

## Overview

**This edition features the exact same content as the traditional book in a convenient, three-hole- punched, loose-leaf version. Books a la Carte also offer a great value–this format costs significantly less than a new textbook.**

**Statistics: The Art and Science of Learning from Data, Third Edition,** helps students become statistically literate by encouraging them to ask and answer interesting statistical questions. This book takes the ideas that have turned statistics into a central science in modern life and makes them accessible without compromising necessary rigor. Authors Alan Agresti and Christine Franklin believe that it’s important for students to learn and analyze both quantitative and categorical data. As a result, the book pays greater attention to the analysis of proportions than many other introductory statistics books. Concepts are introduced first with categorical data, and then with quantitative data.

The **Third Edition** has been edited for conciseness and clarity to keep students focused on the main concepts. The data-rich examples that feature intriguing human-interest topics now include topic labels to indicate which statistical topic is being applied. New learning objectives for each chapter appear in the Instructor’s Edition, making it easier to plan lectures and Chapter 7 (Sampling Distributions) now incorporates simulations in addition to the mathematical formulas.

**This package contains:**

**Books a la Carte for Statistics: The Art and Science of Learning from Data, Third Edition**, plus the CD-ROM that comes with the bound version of the textbook.

## Table of contents

**Part 1: Gathering and Exploring Data**

**1. ****Statistics: The Art and Science of Learning from Data**

1.1 Using Data to Answer Statistical Questions

1.2 Sample Versus Population

1.3 Using Calculators and Computers

Chapter Summary

Chapter Problems

**2. Exploring Data with Graphs and Numerical Summaries**

2.1 Different Types of Data

2.2 Graphical Summaries of Data

2.3 Measuring the Center of Quantitative Data

2.4 Measuring the Variability of Quantitative Data

2.5 Using Measures of Position to Describe Variability

2.6 Recognizing and Avoiding Misuses of Graphical Summaries

Chapter Summary

Chapter Problems

**3. ****Association: Contingency, Correlation, and Regression**

3.1 The Association Between Two Categorical Variables

3.2 The Association Between Two Quantitative Variables

3.3 Predicting the Outcome of a Variable

3.4 Cautions in Analyzing Associations

Chapter Summary

Chapter Problems

**4. ****Gathering Data**

4.1 Experimental and Observational Studies

4.2 Good and Poor Ways to Sample

4.3 Good and Poor Ways to Experiment

4.4 Other Ways to Conduct Experimental and Nonexperimental Studies

Chapter Summary

Chapter Problems

Part 1 Review

Part 1 Questions

Part 1 Exercises

**Part 2: ****Probability, Probability Distributions, and Sampling Distributions**

**5. Probability in Our Daily Lives**

5.1 How Probability Quantifies Randomness

5.2 Finding Probabilities

5.3 Conditional Probability: The Probability of A Given B

5.4 Applying the Probability Rules

Chapter Summary

Chapter Problems

**6. ****Probability Distributions**

6.1 Summarizing Possible Outcomes and Their Probabilities

6.2 Probabilities for Bell-Shaped Distributions

6.3 Probabilities When Each Observation Has Two Possible Outcomes

Chapter Summary

Chapter Problems

**7. Sampling Distributions**

7.1 How Sample Proportions Vary Around the Population Proportion

7.2 How Sample Means Vary Around the Population Mean

7.3 The Binomial Distribution Is a Sampling Distribution (Optional)

Chapter Summary

Chapter Problems

Part 2 Review

Part 2 Questions

Part 2 Exercises

**Part 3: Inferential Statistics**

**8. Statistical Inference: Confidence Intervals**

8.1 Point and Interval Estimates of Population Parameters

8.2 Constructing a Confidence Interval to Estimate a Population Proportion

8.3 Constructing a Confidence Interval to Estimate a Population Mean

8.4 Choosing the Sample Size for a Study

8.5 Using Computers to Make New Estimation Methods Possible

Chapter Summary

Chapter Problems

**9. ****Statistical Inference: Significance Tests about Hypotheses**

9.1 Steps for Performing a Significance Test

9.2 Significance Tests about Proportions

9.3 Significance Tests about Means

9.4 Decisions and Types of Errors in Significance Tests

9.5 Limitations of Significance Tests

9.6 The Likelihood of a Type II Error (Not Rejecting H_{0}, Even Though It’s False)

Chapter Summary

Chapter Problems

**10. Comparing Two Groups**

10.1 Categorical Response: Comparing Two Proportions

10.2 Quantitative Response: Comparing Two Means

10.3 Other Ways of Comparing Means and Comparing Proportions

10.4 Analyzing Dependent Samples

10.5 Adjusting for the Effects of Other Variables

Chapter Summary

Chapter Problems

Part 3 Review

Part 3 Questions

Part 3 Exercises

**Part 4: Analyzing Association and Extended Statistical Methods**

**11. Analyzing the Association Between Categorical Variables**

11.1 Independence and Association

11.2 Testing Categorical Variables for Independence

11.3 Determining the Strength of the Association

11.4 Using Residuals to Reveal the Pattern of Association

11.5 Small Sample Sizes: Fisher’s Exact Test

Chapter Summary

Chapter Problems

**12. Analyzing the Association Between Quantitative Variables: Regression Analysis**

12.1 Model How Two Variables Are Related

12.2 Describe Strength of Association

12.3 Make Inference About the Association

12.4How the Data Vary Around the Regression Line

12.5 Exponential Regression: A Model for Nonlinearity

Chapter Summary

Chapter Problems

**13. Multiple Regression**

13.1 Using Several Variables to Predict a Response

13.2 Extending the Correlation and R-squared for Multiple Regression

13.3 Using Multiple Regression to Make Inferences

13.4 Checking a Regression Model Using Residual Plots

13.5 Regression and Categorical Predictors

13.6 Modeling a Categorical Response

Chapter Summary

Chapter Problems

**14. Comparing Groups: Analysis of Variance Methods**

14.1 One-Way ANOVA: Comparing Several Means

14.2 Estimating Differences in Groups for a Single Factor

14.3 Two-Way ANOVA

Chapter Summary

Chapter Problems

15. Nonparametric Statistics

15.1 Compare Two Groups by Ranking

15.2 Nonparametric Methods For Several Groups and for Matched Pairs

Chapter Summary

Chapter Problems

PART 4 Review

Part 4 Questions

Part 4 Exercises

Tables

Answers

Index

Index of Applications

Photo Credits

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