Product Cover Image

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

By Alan Agresti, Christine A. Franklin

Published by Pearson

Published Date: Feb 29, 2008

More Product Info

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.

Table of Contents

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?

            Answers to Chapter Figure Questions

            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?

            Answers to Chapter Figure Questions

            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?

            Answers to Chapter Figure Questions

            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

            Answers to Chapter Figure Questions

            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?

            Answers to Chapter Figure Questions

            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?

            Answers to Chapter Figure Questions

            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?

            Answers to Chapter Figure Questions

            Chapter Summary

            Chapter Exercises

 

9. Statistical Inference: Significance Tests about Hypotheses

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

            Answers to Chapter Figure Questions

            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?

            Answers to Chapter Figure Questions

            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

            Answers to Chapter Figure Questions

            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

            Answers to Chapter Figure Questions

            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?

            Answers to Chapter Figure Questions

            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

            Answers to Chapter Figure Questions

            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

            Answers to Chapter Figure Questions

            Chapter Summary

            Chapter Exercises

 

            PART 4 REVIEW

            Part 4 Summary

            Part 4 Exercises

 

Tables

Selected Answers

Index

Index of Applications     

Photo Credits

Purchase Info ?

With CourseSmart eTextbooks and eResources, you save up to 60% off the price of new print textbooks, and can switch between studying online or offline to suit your needs.

Once you have purchased your eTextbooks and added them to your CourseSmart bookshelf, you can access them anytime, anywhere.

Buy Access

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

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