Introductory statistics : exploring the world through data

Cover
Title Page
Copyright Page
Dedication
About the Authors
Contents
Preface
Index of Applications
CHAPTER 1 Introduction to Data
	CASE STUDY: Dangerous Habit?
	1.1 What Are Data?
	1.2 Classifying and Storing Data
	1.3 Investigating Data
	1.4 Organizing Categorical Data
	1.5 Collecting Data to Understand Causality
	DATA PROJECT: How Are Data Stored?
CHAPTER 2 Picturing Variation with Graphs
	CASE STUDY: Student-to-Teacher Ratio at Colleges
	2.1 Visualizing Variation in Numerical Data
	2.2 Summarizing Important Features of a Numerical Distribution
	2.3 Visualizing Variation in Categorical Variables
	2.4 Summarizing Categorical Distributions
	2.5 Interpreting Graphs
	DATA PROJECT: Asking Questions
CHAPTER 3 Numerical Summaries of Center and Variation
	CASE STUDY: Living in a Risky World
	3.1 Summaries for Symmetric Distributions
	3.2 What’s Unusual? The Empirical Rule and z-Scores
	3.3 Summaries for Skewed Distributions
	3.4 Comparing Measures of Center
	3.5 Using Boxplots for Displaying Summaries
	DATA PROJECT: The Statistical Investigation Cycle
CHAPTER 4 Regression Analysis: Exploring Associations between Variables
	CASE STUDY: Forecasting Home Prices
	4.1 Visualizing Variability with a Scatterplot
	4.2 Measuring Strength of Association with Correlation
	4.3 Modeling Linear Trends
	4.4 Evaluating the Linear Model
	DATA PROJECT: Data Moves
CHAPTER 5 Modeling Variation with Probability
	CASE STUDY: SIDS or Murder?
	5.1 What Is Randomness?
	5.2 Finding Theoretical Probabilities
	5.3 Associations in Categorical Variables
	5.4 Finding Empirical and Simulated Probabilities
	DATA PROJECT: Subsetting Data
CHAPTER 6 Modeling Random Events: The Normal and Binomial Models
	CASE STUDY: You Sometimes Get More Than You Pay for
	6.1 Probability Distributions Are Models of Random Experiments
	6.2 The Normal Model
	6.3 The Binomial Model
	DATA PROJECT: Generating Random Numbers
CHAPTER 7 Survey Sampling and Inference
	CASE STUDY: Spring Break Fever: Just What the Doctors Ordered?
	7.1 Learning about the World through Surveys
	7.2 Measuring the Quality of a Survey
	7.3 The Central Limit Theorem for Sample Proportions
	7.4 Estimating the Population Proportion with Confidence Intervals
	7.5 Comparing Two Population Proportions with Confidence
	DATA PROJECT: Population Proportions
CHAPTER 8 Hypothesis Testing for Population Proportions
	CASE STUDY: Dodging the Question
	8.1 The Essential Ingredients of Hypothesis Testing
	8.2 Hypothesis Testing in Four Steps
	8.3 Hypothesis Tests in Detail
	8.4 Comparing Proportions from Two Populations
	DATA PROJECT: Dates as Data
CHAPTER 9 Inferring Population Means
	CASE STUDY: You Look Sick! Are You Sick?
	9.1 Sample Means of Random Samples
	9.2 The Central Limit Theorem for Sample Means
	9.3 Answering Questions about the Mean of a Population
	9.4 Hypothesis Testing for Means
	9.5 Comparing Two Population Means
	9.6 Overview of Analyzing Means
	DATA PROJECT: Data Structures
CHAPTER 10 Associations between Categorical Variables
	CASE STUDY: Popping Better Popcorn
	10.1 The Basic Ingredients for Testing with Categorical Variables
	10.2 The Chi-Square Test for Goodness of Fit
	10.3 Chi-Square Tests for Associations between Categorical Variables
	10.4 Hypothesis Tests When Sample Sizes Are Small
	DATA PROJECT: Stacking Data
CHAPTER 11 Multiple Comparisons and Analysis of Variance
	CASE STUDY: Seeing Red
	11.1 Multiple Comparisons
	11.2 The Analysis of Variance
	11.3 The ANOVA Test
	11.4 Post Hoc Procedures
	DATA PROJECT: Where to Begin
CHAPTER 12 Experimental Design: Controlling Variation
	CASE STUDY: Does Stretching Improve Athletic Performance?
	12.1 Variation Out of Control
	12.2 Controlling Variation in Surveys
	12.3 Reading Research Papers
	DATA PROJECT: Keep It Real
CHAPTER 13 Inference without Normality
	CASE STUDY: Contagious Yawns
	13.1 Transforming Data
	13.2 The Sign Test for Paired Data
	13.3 Mann-Whitney Test for Two Independent Groups
	13.4 Randomization Tests
	DATA PROJECT: Making Maps and Slicing Strings
CHAPTER 14 Inference for Regression
	CASE STUDY: Another Reason to Stand at Your Desk?
	14.1 The Linear Regression Model
	14.2 Using the Linear Model
	14.3 Predicting Values and Estimating Means
	DATA PROJECT: Think Small
Appendix A: Tables
Appendix B: Answers to Odd-Numbered Exercises
Appendix C: Credits
Index
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