Statistics: The Art and Science of Learning from Data / Edition 4

by Alan Agresti, Christine Franklin, Bernhard Klingenberg

Overview

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For courses in introductory statistics.

The Art and Science of Learning from Data

Statistics: The Art and Science of Learning from Data, Fourth Edition, 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.

Also available with MyStatLab

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Product Details

ISBN-13:	9780133860825
Publisher:	Pearson Education
Publication date:	01/07/2016
Edition description:	4th ed.
Pages:	816
Product dimensions:	8.50(w) x 10.80(h) x 1.00(d)
Lexile:	1210L (what's this?)

About the Author

Alan Agresti is Distinguished Professor Emeritus in the Department of Statistics at the University of Florida. He taught statistics there for 38 years, including the development of three courses in statistical methods for social science students and three courses in categorical data analysis. He is author of more than 100 refereed articles and six texts, including Statistical Methods for the Social Sciences (Pearson, 5th edition, 2018) and An Introduction to Categorical Data Analysis (Wiley, 3rd edition, 2019). He is a Fellow of the American Statistical Association and recipient of an Honorary Doctor of Science from De Montfort University in the UK. He has held visiting positions at Harvard University, Boston University, the London School of Economics, and Imperial College and has taught courses or short courses for universities and companies in about 30 countries worldwide. He has also received teaching awards from the University of Florida and an excellence in writing award from John Wiley & Sons.

Christine Franklin is the K-12 Statistics Ambassador for the American Statistical Association and elected ASA Fellow. She is retired from the University of Georgia as the Lothar Tresp Honoratus Honors Professor and Senior Lecturer Emerita in Statistics. She is the co-author of two textbooks and has published more than 60 journal articles and book chapters. Chris was the lead writer for American Statistical Association Pre-K-12 Guidelines for the Assessment and Instruction in Statistics Education (GAISE) Framework document, co-chair for the updated Pre-K-12 GAISE II, and chair of the ASA Statistical Education of Teachers (SET) report. She is a past Chief Reader for Advance Placement Statistics, a Fulbright scholar to New Zealand (2015), recipient of the United States Conference on Teaching Statistics (USCOTS) Lifetime Achievement Award, the ASA Founder’s award and an elected member of the International Statistical Institute (ISI). Chris loves being with her family, running, hiking, scoring baseball games, and reading mysteries.

Bernhard Klingenberg is Professor of Statistics in the Department of Mathematics & Statistics at Williams College, where he has been teaching introductory and advanced statistics classes since 2004, and in the Graduate Data Science Program at New College of Florida, where he enjoys teaching statistical inference and modeling as well as data visualization. Bernhard is responsible for the development of the web apps, which he programs using the R package shiny. A native of Austria, Bernhard frequently returns there to hold visiting positions at universities and gives short courses on categorical data analysis in Europe and the United States. He has published several peer-reviewed articles in statistical journals and consults regularly with academia and industry. Bernhard enjoys photography (some of his pictures appear in this book), scuba diving, hiking state parks in Florida, and spending time with his wife and four children.

Table of Contents

Table of Contents

• 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

1. 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
2. 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
3. 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

1. 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
2. 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 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
3. 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

1. 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
2. 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
3. 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
4. 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
5. 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

Answers

Index

Index of Applications

Credits