Stats: Data and Models, 5th edition
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Overview
Stats: Data and Models uses technology, innovative methods, and a sense of humor—maintaining core concepts, coverage, and most importantly, readability.
Published by Pearson (July 15th 2020)  Copyright © 2020
ISBN13: 9780136880790
Subject: Introductory Statistics
Category: Stats: Data and Models
Table of contents
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 ReExpressing 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
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 R^{2}: 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 ReExpression
 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
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 Want
 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
IV. RANDOMNESS AND PROBABILITY
 12. From Randomness to Probability
 12.1 Random Phenomena
 12.2 Modeling Probability
 12.3 Formal Probability
 13. Probability Rules!
 13.1 The General Addition Rule
 13.2 Conditional Probability and the General Multiplication Rule
 13.3 Independence
 13.4 Picturing Probability: Tables, Venn Diagrams, and Trees
 13.5 Reversing the Conditioning and Bayes' Rule
 14. Random Variables
 14.1 Center: The Expected Value
 14.2 Spread: The Standard Deviation
 14.3 Shifting and Combining Random Variables
 14.4 Continuous Random Variables
 15. Probability Models
 15.1 Bernoulli Trials
 15.2 The Geometric Model
 15.3 The Binomial Model
 15.4 Approximating the Binomial with a Normal Model
 15.5 The Continuity Correction
 15.6 The Poisson Model
 15.7 Other Continuous Random Variables: The Uniform and the Exponential
 Review of Part IV: Randomness and Probability
V. INFERENCE FOR ONE PARAMETER
 16. Sampling Distribution Models and Confidence Intervals for Proportions
 16.1 The Sampling Distribution Model for a Proportion
 16.2 When Does the Normal Model Work? Assumptions and Conditions
 16.3 A Confidence Interval for a Proportion
 16.4 Interpreting Confidence Intervals: What Does 95% Confidence Really Mean?
 16.5 Margin of Error: Certainty vs. Precision
 16.6 Choosing the Sample Size
 17. Confidence Intervals for Means
 17.1 The Central Limit Theorem
 17.2 A Confidence Interval for the Mean
 17.3 Interpreting Confidence Intervals
 17.4 Picking Our Interval up by Our Bootstraps
 17.5 Thoughts About Confidence Intervals
 18. Testing Hypotheses
 18.1 Hypotheses
 18.2 PValues
 18.3 The Reasoning of Hypothesis Testing
 18.4 A Hypothesis Test for the Mean
 18.5 Intervals and Tests
 18.6 PValues and Decisions: What to Tell About a Hypothesis Test
 19. More About Tests and Intervals
 19.1 Interpreting PValues
 19.2 Alpha Levels and Critical Values
 19.3 Practical vs. Statistical Significance
 19.4 Errors
 Review of Part V: Inference for One Parameter
VI. INFERENCE FOR RELATIONSHIPS
 20. Comparing Groups
 20.1 A Confidence Interval for the Difference Between Two Proportions
 20.2 Assumptions and Conditions for Comparing Proportions
 20.3 The TwoSample zTest: Testing for the Difference Between Proportions
 20.4 A Confidence Interval for the Difference Between Two Means
 20.5 The TwoSample tTest: Testing for the Difference Between Two Means
 20.6 Randomization Tests and Confidence Intervals for Two Means
 20.7 Pooling
 20.8 The Standard Deviation of a Difference
 21. Paired Samples and Blocks
 21.1 Paired Data
 21.2 The Paired tTest
 21.3 Confidence Intervals for Matched Pairs
 21.4 Blocking
 22. Comparing Counts
 22.1 GoodnessofFit Tests
 22.2 ChiSquare Test of Homogeneity
 22.3 Examining the Residuals
 22.4 ChiSquare Test of Independence
 23. Inferences for Regression
 23.1 The Regression Model
 23.2 Assumptions and Conditions
 23.3 Regression Inference and Intuition
 23.4 The Regression Table
 23.5 Multiple Regression Inference
 23.6 Confidence and Prediction Intervals
 23.7 Logistic Regression
 23.8 More About Regression
 Review of Part VI: Inference for Relationships
VII. INFERENCE WHEN VARIABLES ARE RELATED
 24. Multiple Regression Wisdom
 24.1 Multiple Regression Inference
 24.2 Comparing Multiple Regression Model
 24.3 Indicators
 24.4 Diagnosing Regression Models: Looking at the Cases
 24.5 Building Multiple Regression Models
 25. Analysis of Variance
 25.1 Testing Whether the Means of Several Groups Are Equal
 25.2 The ANOVA Table
 25.3 Assumptions and Conditions
 25.4 Comparing Means
 25.5 ANOVA on Observational Data
 26. Multifactor Analysis of Variance
 26.1 A Two Factor ANOVA Model
 26.2 Assumptions and Conditions
 26.3 Interactions
 27. Statistics and Data Science
 27.1 Introduction to Data Mining
 Review of Part VII: Inference When Variables Are Related
Parts I  V Cumulative Review Exercises
Appendices
 Answers
 Credits
 Indexes
 Tables and Selected Formulas
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