Intro Stats

• Random Matters: This new feature encourages a gradual, cumulative understanding of randomization. The first Random Matters box introduces drawing inferences from data. Subsequent Random Matters features draw histograms of sample means, introduce the thinking involved in permutation tests, and encourage judgment about how likely the observed statistic seems when viewed against the simulated sampling distribution of the null hypothesis.

• Streamlined coverage of descriptive statistics helps students progress more quickly through the first part of the book. Also a GAISE recommendation, random variables and probability distributions are now covered later in the text to allow for more time on the more critical statistical concepts.

• Technology is utilized to improve the learning of two of the most difficult concepts in the introductory course: the idea of a sampling distribution and the reasoning of statistical inference.

Supports learning through worked examples and practice opportunities

• A third variable is introduced with contingency tables and mosaic plots in Chapter 3 to give students earlier experience with multivariable thinking. Then, following the discussion of correlation and regression as a tool (without inference) in Chapters 6, 7, and 8, multiple regression is introduced in Chapter 9.

• Expanded and revised Think, Show, Tell examples guide students through the process of analyzing a problem through worked examples. They illustrate the importance of thinking about a statistics question (Think) and reporting findings (Tell)). The Show step contains the mechanics of calculating results. This results in a better understanding of the concept and problem-solving process that goes beyond number crunching.

• New Web tools that provide interactive versions of the distribution tables at the back of the book and tools for randomization inference methods such as the bootstrap and for repeated sampling from larger populations can be found online at astools.datadesk.com.

• NEW! Random Matters: This new feature encourages a gradual, cumulative understanding of randomization. The first Random Matters box introduces drawing inferences from data. Subsequent Random Matters features draw histograms of sample means, introduce the thinking involved in permutation tests, and encourage judgment about how likely the observed statistic seems when viewed against the simulated sampling distribution of the null hypothesis.

• NEW! Streamlined coverage of descriptive statistics helps students progress more quickly through the first part of the book. Also a GAISE recommendation, random variables and probability distributions are now covered later in the text to allow for more time on the more critical statistical concepts.

• NEW! Technology is utilized to improve the learning of two of the most difficult concepts in the introductory course: the idea of a sampling distribution and the reasoning of statistical inference.

• NEW! A third variable is introduced with contingency tables and mosaic plots in Chapter 3 to give students earlier experience with multivariable thinking. Then, following the discussion of correlation and regression as a tool (without inference) in Chapters 6, 7, and 8, multiple regression is introduced in Chapter 9.

• Where Are We Going? chapter openers give a context for the work students are about to begin within the broader course.

• Margin and in-text boxed notes throughout each chapter enhance and enrich the text.

• Reality Checks ask students to think about whether their answers make sense before interpreting their results.

• Notation Alerts appear whenever special notation is introduced.

• The Tech Support section provides instructions for applying the topics covered by the chapter within each of the supported statistics packages.

Supports learning through worked examples and practice opportunities

• UPDATED! Expanded and revised Think, Show, Tell examples guide students through the process of analyzing a problem through worked examples. They illustrate the importance of thinking about a statistics question (Think) and reporting findings (Tell)). The Show step contains the mechanics of calculating results. This results in a better understanding of the concept and problem-solving process that goes beyond number crunching.

• Focused examples are provided as each important concept is introduced, applying the concept usually with real, up-to-the-minute data. Many examples carry the discussion through the chapter, picking up the story and moving it forward as students learn more about the topic.

• Just Checking questions are quick checks throughout the chapter that involve minimal calculation and encourage students to pause and think about what they’ve just read. The Just Checking answers are at the end of the exercise sets in each chapter so students can easily check themselves.

• End-of-chapter material includes:

• Connections sections that specifically ties the new topics to those learned in previous chapters.

• What Can Go Wrong? sections that highlight the most common errors that people make and the misconceptions they have about statistics. One of our goals is to arm students with the tools to detect statistical errors and to offer practice in debunking misuses of  statistics., whether intentional or not.

• Chapter Reviews that summarizes the story told by the chapter and provides a bulleted lists of the major concepts and principles covered.

• A Review of Terms glossary of all of the boldfaced terms introduced in the chapter including. The Review provides page references, so students can easily turn back to a full discussion of the term if the brief definition isn’t sufficient.

• Abundant exercises at the end of each chapter start with relatively simple, focused exercises for each chapter section and move on to more extensive exercises that may deal with topics from several parts of the chapter or even from previous chapters as they combine with the topics of the chapter at hand. All exercises appear in pairs, and odd-numbered exercises have answers in the back of student texts. Each even-numbered exercise covers the same topic (although not in exactly the same way) as the previous odd exercise.

• More than 600 of the exercises include an icon indicating that the dataset referenced is available electronically. The exercise title or a note provides the dataset title. Some exercises are tagged to indicate that they call for the student to generate random samples or use randomization methods such as the bootstrap.

• Part Reviews discuss the concepts in each part of the text, tying them together and summarizing the material.

• Additional exercises follow the Part Reviews; these are not paired and not tied to a chapter, making them more like potential exam questions and a good tool for review.

• Parts I-V Cumulative Review Exercises comprise a final book-level review section towards the end of the text. Cumulative Review exercises are longer and cover concepts from the book as a whole.

Most of the text’s supporting materials can be found online at the book’s website or within the MyLab Statistics course.

• Datasets are also available at dasl.datadesk.com.

• The Data Desk statistics program is available from datadesk.com.

• NEW! Web tools that provide interactive versions of the distribution tables at the back of the book and tools for randomization inference methods such as the bootstrap and for repeated sampling from larger populations can be found online at astools.datadesk.com.

2.1 Summarizing and Displaying a Categorical Variable
2.2 Displaying a Quantitative variable
2.3 Shape
2.4 Center
2.5 Spread

3.1 Contingency tables
3.2 Conditional distributions
3.3 Displaying Contingency Tables
3.4 Three Categorical Variables

4.1 Displays for Comparing Groups
4.2 Outliers
4.3 Re-Expressing Data: A First Look

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

7.1 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

8.1 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

9.1 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

11.1 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

13.1 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

14.1 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 

15.1 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

16.1 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

18.1 Paired Data
18.2 Assumptions and Conditions
18.3 Confidence Intervals for Matched Pairs
18.4 Blocking

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

20.1 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