## Description

**Solution Manual (Download Only) For Intro Stats Plus MyLab Statistics with Pearson eText 5th Edition By Richard D. De Veaux, Paul F. Velleman, David E. Bock **

**ISBN-10: 0134210239, ISBN-13: 9780134210230**

Table of Contents

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 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

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

* Indicates optional section