Intro Stats

Cover
Title Page
Copyright Page
Dedication
Meet the Authors
Table of Contents
Preface
Acknowledgments
Index of Applications
PART I Exploring and Understanding Data
	1 Stats Starts Here
		1.1 What Is Statistics?
		1.2 Data
		1.3 Variables
		1.4 Models
	2 Displaying and Describing Data
		2.1 Summarizing and Displaying a Categorical Variable
		2.2 Displaying a Quantitative Variable
		2.3 Shape
		2.4 Center
		2.5 Spread
	3 Relationships Between Categorical Variables—Contingency Tables
		3.1 Contingency Tables
		3.2 Conditional Distributions
		3.3 Displaying Contingency Tables
		3.4 Three Categorical Variables
	4 Understanding and Comparing Distributions
		4.1 Displays for Comparing Groups
		4.2 Outliers
		4.3 Re-Expressing Data: A First Look
	5 The Standard Deviation as a Ruler and the Normal Model
		5.1 Using the Standard Deviation to Standardize Values
		5.2 Shifting and Scaling
		5.3 Normal Models
		5.4 Working with Normal Percentiles
		5.5 Normal Probability Plots
	Review of Part I: Exploring and Understanding Data
PART II Exploring Relationships Between Variables
	6 Scatterplots, Association, and Correlation
		6.1 Scatterplots
		6.2 Correlation
		6.3 Warning: Correlation ≠ Causation
		*6.4 Straightening Scatterplots
	7 Linear Regression
		7.1 Least Squares: The Line of “Best Fit”
		7.2 The Linear Model
		7.3 Finding the Least Squares Line
		7.4 Regression to the Mean
		7.5 Examining the Residuals
		7.6 R²—The Variation Accounted For by the Model
		7.7 Regression Assumptions and Conditions
	8 Regression Wisdom
		8.1 Examining Residuals
		8.2 Extrapolation: Reaching Beyond the Data
		8.3 Outliers, Leverage, and Influence
		8.4 Lurking Variables and Causation
		8.5 Working with Summary Values
		8.6 Straightening Scatterplots—The Three Goals
		*8.7 Finding a Good Re-Expression
	9 Multiple Regression
		9.1 What Is Multiple Regression?
		9.2 Interpreting Multiple Regression Coefficients
		9.3 The Multiple Regression Model—Assumptions and Conditions
		9.4 Partial Regression Plots
		*9.5 Indicator Variables
	Review of Part II: Exploring Relationships Between Variables
PART III Gathering Data
	10 Sample Surveys
		10.1 The Three Big Ideas of Sampling
		10.2 Populations and Parameters
		10.3 Simple Random Samples
		10.4 Other Sampling Designs
		10.5 From the Population to the Sample:You Can’t Always Get What You Want⁷
		10.6 The Valid Survey
		10.7 Common Sampling Mistakes, or How to Sample Badly
	11 Experiments and Observational Studies
		11.1 Observational Studies
		11.2 Randomized, Comparative Experiments
		11.3 The Four Principles of Experimental Design
		11.4 Control Groups
		11.5 Blocking
		11.6 Confounding
	Review of Part III: Gathering Data
PART IV From the Data at Hand to the World at Large
	12 From Randomness to Probability
		12.1 Random Phenomena
		12.2 Modeling Probability
		12.3 Formal Probability
		12.4 Conditional Probability and the General Multiplication Rule
		12.5 Independence
		12.6 Picturing Probability: Tables, Venn Diagrams, and Trees
		12.7 Reversing the Conditioning and Bayes’ Rule
	13 Sampling Distribution Models and Confidence Intervals for Proportions
		13.1 The Sampling Distribution Model for a Proportion
		13.2 When Does the Normal Model Work? Assumptions and Conditions
		13.3 A Confidence Interval for a Proportion
		13.4 Interpreting Confidence Intervals: What Does 95% Confidence Really Mean?
		13.5 Margin of Error: Certainty vs. Precision
		*13.6 Choosing the Sample Size
	14 Confidence Intervals for Means
		14.1 The Central Limit Theorem
		14.2 A Confidence Interval for the Mean
		14.3 Interpreting Confidence Intervals
		*14.4 Picking Our Interval Up by Our Bootstraps
		14.5 Thoughts About Confidence Intervals
	15 Testing Hypotheses
		15.1 Hypotheses
		15.2 P-Values
		15.3 The Reasoning of Hypothesis Testing
		15.4 A Hypothesis Test for the Mean
		15.5 Intervals and Tests
		15.6 P-Values and Decisions: What to Tell About a Hypothesis Test
	16 More About Tests and Intervals
		16.1 Interpreting P-Values
		16.2 Alpha Levels and Critical Values
		16.3 Practical vs. Statistical Significance
		16.4 Errors
	Review of Part IV: From the Data at Hand to the World at Large
PART V Inference for Relationships
	17 Comparing Groups
		17.1 A Confidence Interval for the Difference Between Two Proportions
		17.2 Assumptions and Conditions for Comparing Proportions
		17.3 The Two-Sample z-Test: Testing the Difference Between Proportions
		17.4 A Confidence Interval for the Difference Between Two Means
		17.5 The Two-Sample t-Test: Testing for the Difference Between Two Means
		*17.6 Pooling
		*17.7 The Standard Deviation of a Difference
	18 Paired Samples and Blocks
		18.1 Paired Data
		18.2 The Paired t-Test
		18.3 Confidence Intervals for Matched Pairs
		18.4 Blocking
	19 Comparing Counts
		19.1 Goodness-of-Fit Tests
		19.2 Chi-Square Test of Homogeneity
		19.3 Examining the Residuals
		19.4 Chi-Square Test of Independence
	20 Inferences for Regression
		20.1 The Regression Model
		20.2 Assumptions and Conditions
		20.3 Regression Inference and Intuition
		20.4 The Regression Table
		20.5 Multiple Regression Inference
		20.6 Confidence and Prediction Intervals
		*20.7 Logistic Regression
		*20.8 More About Regression
	Review of Part V: Inference for Relationships
	Parts I–V Cumulative Review Exercises
Appendixes
	A Answers
	B Credits
	C Indexes
		Datasets Index
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		Subject Index
			Notation
			Numbers
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	D Tables and Selected Formulas