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Intro Stats, 6th edition

  • Richard D. De Veaux, 
  • Paul F. Velleman, 
  • David E. Bock

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Overview

Intro Stats uses innovative methods, technology and humor to help you think critically about data. Technology and simulations help demonstrate variability at critical points, making it easier for you to understand more complex statistical concepts later in the course. The 6th Edition includes several enhancements, enriching the material with the authors' signature tools for teaching about randomness, sampling distribution models, and inference. Current discussions of ethical issues have been added throughout, and each chapter now ends with a student project that can be used for collaborative work. A student version of Data Desk RP, a statistics program with a graphical interface that is easy to learn and use, is available at datadesk.com.

Published by Pearson (July 26th 2021) - Copyright © 2022

ISBN-13: 9780137374922

Subject: Introductory Statistics

Category: Intro Stats

Overview

  • *Indicates optional section

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

II: EXPLORING RELATIONSHIPS BETWEEN VARIABLES

  1. Scatterplots, Association, and Correlation
    • 6.1 Scatterplots
    • 6.2 Correlation
    • 6.3 Warning: Correlation ≠ Causation
    • 6.4 *Straightening Scatterplots
  2. 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
  3. 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
  4. 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

  1. 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
  2. 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: FROM THE DATA AT HAND TO THE WORLD AT LARGE

  1. 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
  2. Sampling Distributions and Confidence Intervals for Proportions
    • 13.1 The Sampling Distribution 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
  3. 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
  4. 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
  5. 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

V: INFERENCE FOR RELATIONSHIPS

  1. 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 * Randomization-Based Tests and Confidence Intervals for Two Means
    • 17.7 * Pooling
    • 17.8 * The Standard Deviation of a Difference
  2. Paired Samples and Blocks
    • 18.1 Paired Data
    • 18.2 The Paired t-Test
    • 18.3 Confidence Intervals for Matched Pairs
    • 18.4 Blocking
  3. Comparing Counts
    • 19.1 Goodness-of-Fit Tests
    • 19.2 Chi-Square Tests of Homogeneity
    • 19.3 Examining the Residuals
    • 19.4 Chi-Square Test of Independence
  4. 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:

  1. Answers
  2. Credits
  3. Indexes
  4. Tables and Selected Formulas

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