# Statistics: The Art and Science of Learning from Data, CourseSmart eTextbook, 2nd Edition

Published Date: Feb 29, 2008

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## Description

Alan Agresti and Chris Franklin have merged their research and classroom experience to develop this successful introductory statistics text. Statistics: The Art and Science of Learning from Data, Second Edition helps students become statistically literate by encouraging them to ask and answer interesting statistical questions. It takes the ideas that have turned statistics into a central science in modern life and makes them accessible and engaging to students without compromising necessary rigor.

The varied and data-rich examples and exercises place heavy emphasis on thinking about and understanding statistical concepts. The applications are topical and current, and successfully illustrate the relevance of statistics. The authors believe that it is important for students to be comfortable with analyzing both quantitative and categorical data. Every day in the media, students see and hear percentages and rates being used to summarize opinion polls, outcomes of medical studies, and economic reports. As a result, greater attention is paid to the analysis of proportions than is typical of many introductory statistic texts.

The text maintains its commitment to the recommendations of the ASA endorsed GAISE (Guidelines for Assessment for Instruction in Statistical Education) Report.

Datasets and other resources (where applicable) for this book are available here.

CourseSmart textbooks do not include any media or print supplements that come packaged with the bound book.

PART 1: GATHERING and EXPLORING DATA

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

1.1 How Can You Investigate Using Data?

1.2 We Learn about Population Using Samples

1.3 What Role do Computers Play in Statistics?

Chapter Summary

Chapter Exercises

2. Exploring Data with Graphs and Numerical Summaries

2.1 What Are the Types of Data?

2.2 How Can We Describe Data using Graphical Summaries?

2.3 How Can We Describe the Center of Quantitative Data?

2.4 How Can We Describe the Spread of Quantitative Data?

2.5 How Can Measures of Position Describe Spread?

2.6 How Can Graphical Summaries Be Misused?

Chapter Summary

Chapter Exercises

3. Association: Contingency, Correlation, and Regression

3.1 How Can We Explore the Association between Two Categorical Variables?

3.2 How Can We Explore the Association between Two Quantitative Variables?

3.3 How Can We Predict the Outcome of a Variable?

3.4 What are Some Cautions in Analyzing Associations?

Chapter Summary

Chapter Exercises

4. Gathering Data

4.1 Should We Experiment or Should We Merely Observe?

4.2 What Are Good Ways and Poor Ways to Sample?

4.3 What Are Good Ways and Poor Ways to Experiment?

4.4 What Are Other Ways to Perform Experimental and Nonexperimental Studies?

Chapter Summary

Chapter Exercises

PART 1 REVIEW

Part 1 Summary

Part 1 Exercises

PART 2: PROBABILITY AND PROBABILITY DISTRIBUTIONS

5. Probability in our Daily Lives

5.1 How Can Probability Quantify Randomness?

5.2 How Can We Find Probabilities?

5.3 Conditional Probability: What’s the Probability of A, Given B?

5.4 Applying the Probability Rules

Chapter Summary

Chapter Exercises

6. Probability Distributions

6.1 How Can We Summarize Possible Outcomes and Their Probabilities?

6.2 How Can We Find Probabilities for Bell-Shaped Distributions?

6.3 How Can We Find Probabilities when Each Observation has Two Possible Outcomes?

Chapter Summary

Chapter Exercises

7. Sampling Distributions

7.1 How Likely Are the Possible Values of a Statistics? The Sampling Distribution

7.2 How Close Are Sample Means to Population Means?

7.3 How Can We Make Inferences about a Population?

Chapter Summary

Chapter Exercises

PART 2 REVIEW

Part 2 Summary

Part 2 Exercises

PART 3: INFERENCE STATISTICS

8. Statistical Inference: Confidence Intervals

8.1 What Are Point and Interval Estimates of Population Parameters?

8.2 How Can We Construct a Confidence Interval to Estimate a Population Proportion?

8.3 How Can We Construct a Confidence Interval to Estimate a Population Mean?

8.4 How Do We Choose the Sample Size for a Study?

8.5 How Do Computers Make New Estimation Methods Possible?

Chapter Summary

Chapter Exercises

9. Statistical Inference: Significance Tests about Hypotheses

9.1 What Are the Steps for Performing a Significance Test?

9.4 Decisions and Types of Errors in Significance Tests

9.5 Limitations of Significance Tests

9.6 How Likely is a Type II Error (Not Rejecting H0, Even though it’s False)?

Chapter Summary

Chapter Exercises

10. Comparing Two Groups

10.1 Categorical Response: How Can We Compare Two Proportions?

10.2 Quantitative Response: How Can We Compare Two Means?

10.3 Other Ways of Comparing Means and Comparing Proportions

10.4 How Can We Analyze Dependent Samples?

10.5 How Can We Adjust for Effects of Other Variables?

Chapter Summary

Chapter Exercises

PART 3 REVIEW

Part 3 Summary

Part 3 Exercises

PART 4: ANALYZING ASSOCIATIONS AND EXTENDED STATISTICAL METHODS

11. Analyzing the Association Between Categorical Variables

11.1 What is Independence and What is Association?

11.2 How Can We Test Whether Categorical Variables are Independent?

11.3 How Strong is the Association?

11.4 How Can Residuals Reveal the Pattern of Association?

11.5 What if the Sample Size is Small? Fisher’s Exact Test

Chapter Summary

Chapter Exercises

12. Analyzing the Association Between Quantitative Variables: Regression Analysis

12.1 How Can We “Model” How Two Variables Are Related?

12.2 How Can We Describe Strength of Association?

12.3 How Can We Make Inferences about the Association?

12.4 What Do We Learn from How the Data Vary around the Regression Line?

12.5 Exponential Regression: A Model for Nonlinearity

Chapter Summary

Chapter Exercises

13. Multiple Regression

13.1 How Can We Use Several Variables to Predict a Response?

13.2 Extending the Correlation and R-squared for Multiple Regression

13.3 How Can We Use Multiple Regression to Make Inferences?

13.4 Checking a Regression Model Using Residual Plots

13.5 How Can Regression Include Categorical Predictors?

13.6 How Can We Model a Categorical Response?

Chapter Summary

Chapter Exercises

14. Comparing Groups: Analysis of Variance Methods

14.1 How Can We Compare Several Means?: One-Way ANOVA

14.2 How Should We Follow Up an ANOVA F Test

14.3 What if there are Two Factors?: Two-way ANOVA

Chapter Summary

Chapter Exercises

15. Nonparametric Statistics

15.1 How Can We Compare Two Groups by Ranking?

15.2 Nonparametric Methods for Several Groups and for Matched Pairs

Chapter Summary

Chapter Exercises

PART 4 REVIEW

Part 4 Summary

Part 4 Exercises

Tables

Index

Index of Applications

Photo Credits

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\$70.99 | ISBN-13: 978-0-13-502416-4