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

• Alan Agresti University of Florida
• Christine A. Franklin University of Georgia
• Bernhard Klingenberg Williams College

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For courses in introductory statistics.

### The art and science of learning from data

Statistics, 5th Edition helps students understand what statistics is all about and learn the right questions to ask when analyzing data, rather than just memorize procedures. It conveys the ideas that have turned statistics into an essential science of modern life, without compromising rigor. Students will stay engaged with the real-world data in the examples and exercises. Based on the authors' belief that it's important for students to learn and analyze both quantitative and categorial data, this text pays greater attention to the analysis of proportions than many other introductory statistics texts.

### Hallmark features of this title

• The authors give 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 importance of the statistical investigative process is emphasized in Chapter 1.
• Featured examples and exercises throughout use the most recent data available.
• The approach emphasizes using interval estimation for inference with less reliance on significance testing, and incorporates the 2016 American Statistical Association's statement on P-values.

### New and updated features of this title

• Integrates the online apps from  ArtofStat.com into every chapter through multiple screenshots and activities, inviting the user to replicate results using data from the authors' many examples. These apps can be used live in lectures (and together with students) to visualize concepts or carry out analysis, or can be recorded for asynchronous online instructions. Students can download or screenshot results for homework assignments or projects. At the end of each chapter, a new section on statistical software describes the functionality of each app used in the chapter.
• Streamlined Chapter 1 continues to emphasize the importance of the statistical investigative process and adds an introduction on the opportunities and challenges with Big Data and Data Science, including a discussion of ethical considerations.
• A new section on linear transformations in Chapter 2.
• Emphasis on the two descriptive statistics  most likely encountered by students in their daily lives (differences and ratios of proportions) in Section 3.1.
• Expanded discussion on multivariate thinking  in Section 3.3.
• Significantly expanded coverage of resampling methods,  with a thorough discussion of the bootstrap for one and two-sample problems and for the correlation coefficient, in new Sections 7.3, 8.3, and 10.3.
• 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