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# Intro Stats, 5th edition

Published by Pearson (August 21, 2017) © 2018

**Richard D. De Veaux**Williams College**Paul Velleman**Cornell University**David E. Bock**

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**ISBN-13: 9780134668420**

Intro Stats

Published 2017

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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

Part I Review

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 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 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

Part II Review

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 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 Experiment Design

11.4 Control Groups

11.5 Blocking

11.6 Confounding

Part III Review

PART IV INFERENCE FOR ONE PARAMETER

**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: Bayes’ Rule

**13. 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 your 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

Part IV Review

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 Randomization-Based Tests and Confidence Intervals for Two Means

17.7 *Pooling

17.8 *The Standard Deviation of a Difference

**18. Paired Samples and Blocks**

18.1 Paired Data

18.2 Assumptions and Conditions

18.3 Confidence Intervals for Matched Pairs

18.4 Blocking

**19. 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

**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

Part V Review

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