# Statistics: The Art and Science of Learning from Data, 5th edition

• Alan Agresti
• Christine A. Franklin
• Bernhard Klingenberg

## Overview

For courses in introductory statistics.

This is the 18-week standalone access card for MyLab Statistics.

The art and science of learning from data

Statistics: The Art and Science of Learning from Data takes a conceptual approach, helping students understand what statistics is about and learning the right questions to ask when analyzing data, rather than just memorizing procedures. This book takes the ideas that have turned statistics into a central science in modern life and makes them accessible, without compromising the necessary rigor. Students will enjoy reading this book, and will stay engaged with its wide variety of real-world data in the examples and exercises.

The authors believe that it’s important for students to learn and analyze both quantitative and categorical data. As a result, the text pays greater attention to the analysis of proportions than many other introductory statistics texts. Concepts are introduced first with categorical data, and then with quantitative data. The 5th Edition enhances the student and instructor experience and provides a more accessible introduction to statistical thinking and practice.

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0136559891 / 9780136559894  MYLAB STATISTICS WITH PEARSON ETEXT -- ACCESS CARD -- FOR STATISTICS: THE ART AND SCIENCE OF LEARNING FROM DATA (18-WEEKS), 5/e

Preface

I: 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 Organizing Data, Statistical Software, and the New Field of Data Science

Chapter Summary

Chapter Exercises

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 Linear Transformations and Standardizing

2.7 Recognizing and Avoiding Misuses of Graphical Summaries

Chapter Summary

Chapter Exercises

3. Exploring Relationships Between Two Variables

3.1 The Association Between Two Categorical Variables

3.2 The Relationship Between Two Quantitative Variables

3.3 Linear Regression: Predicting the Outcome of a Variable

3.4 Cautions in Analyzing Associations

Chapter Summary

Chapter Exercises

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 Exercises

II: 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

5.4 Applying the Probability Rules

Chapter Summary

Chapter Exercises

6. Random Variables and 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 Exercises

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 Using the Bootstrap to Find Sampling Distributions

Chapter Summary

Chapter Exercises

III: INFERENTIAL STATISTICS

8. Statistical Inference: Confidence Intervals

8.1 Point and Interval Estimates of Population Parameters

8.2 Confidence Interval for a Population Proportion

8.3 Confidence Interval for a Population Mean

8.4 Bootstrap Confidence Intervals

Chapter Summary

Chapter Exercises

9. Statistical Inference: Significance Tests About Hypotheses

9.1 Steps for Performing a Significance Test

9.3 Significance Tests About a Mean

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

Chapter Summary

Chapter Exercises

10. Comparing Two Groups

10.1 Categorical Response: Comparing Two Proportions

10.2 Quantitative Response: Comparing Two Means

10.3 Comparing Two Groups with Bootstrap or Permutation Resampling

10.4 Analyzing Dependent Samples

10.5 Adjusting for the Effects of Other Variables

Chapter Summary

Chapter Exercises

IV: ANALYZING ASSOCIATION AND EXTENDED STATISTICAL METHODS

11. Analyzing the Association Between Categorical Variables

11.1 Independence and Dependence (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 Fisher’s Exact and Permutation Tests

Chapter Summary

Chapter Exercises

12. Analyzing the Association Between Quantitative Variables: Regression Analysis

12.1 Modeling How Two Variables Are Related

12.2 Inference About Model Parameters and the Association

12.3 Describing the Strength of Association

12.4 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 Using Several Variables to Predict a Response

13.2 Extending the Correlation and R2 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 Exercises

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 Exercises

15. Nonparametric Statistics

15.1 Compare Two Groups by Ranking

15.2 Nonparametric Methods for Several Groups and for Matched Pairs

Chapter Summary

Chapter Exercises

Appendix