- Over 350 New and Updated Exercises The sixth edition makes a concerted effort to require students to write a few sentences that explain the results of their statistical analysis. To reflect this effort, the answers in the back of the text provide recommended explanations of the statistical results. Not all the exercises are computational or require statistical analysis. Many of the exercises have been written to require students to explain statistical concepts or understand pitfalls in faulty statistical analysis.
- Over 100 New and Updated Examples The examples continue to engage and provide clear, concise explanations for the students while following the Problem, Approach, Solution Problem lays out the scenario of the example, Approach provides insight into the thought process behind the methodology used to solve the problem, and Solution goes through the solution utilizing the methodology suggested in the approach.
- Threaded Tornado Problems Throughout the text a single, large data set that measures various variables on all tornadoes that struck the United States in 2017 is utilized. The problems are marked with a The table on the front inside cover shows the sections, problems, and topics covered in the Threaded Tornado Problems. In addition, the author wrote corresponding MyLab problems around this data set. The problems serve as a semester-long project for your students.
|1.1||47, 48||Types of variables; types of data|
|2.1||25||Frequency & relative frequency distributions; bar charts; pie charts|
|2.2||33||Frequency & relative frequency distributions; histogram; dot plots|
|3.1||41||Mean, median, distribution shape|
|3.2||51||Range, standard deviation|
|3.4||29||Quartiles, interquartile range, outliers|
|4.3||31||Scatter diagrams, correlation, least-squares regression, coefficient of determination, residual analysis|
|5.1||49||Probability models; unusual events|
|8.1||33||Describe the distribution of the sample mean from a non-normal population|
|9.1||33||Confidence interval for a population proportion|
|9.2||37||Confidence interval for a population mean|
|10.2||31||Hypothesis test for a population proportion|
|10.3||35||Hypothesis test for a population mean|
|11.1||29||Compare two population proportions (independent samples)|
|11.3||17||Compare two population means (independent samples)|
|13.1||25||One-way Analysis of Variance (ANOVA)|
|14.2||17||Inference on least-squares regression; prediction intervals|
|14.4||12||Indicator (dummy) variables; interaction|
- Updated MyLab Problems New MyLab problems written by Michael Sullivan utilize real data that is randomly generated from a larger data set. He also wrote new applet exercises that allow students to explore statistical concepts.
- Optional Simulation & Randomization Sections Simulation and randomization methods are a new approach to hypothesis testing. New to this edition are optional sections on using simulation to test hypotheses for a population proportion (Section 10.2A) and population mean (Section 10.3A), and randomization methods for testing hypotheses are two independent proportions (Section 11.1A), two independent means (Section 11.3A), and the slope of the least-squares regression model (Section 14.1A).
- Classroom Notes New to this edition are classroom notes, which may be used by the instructor to deliver lectures to students. Students may print these notes out and bring them to the classroom, which facilitates good note-taking and allows them to focus on the concepts. The examples and activities in the classroom notes are different from those in the text and Instructor’s Resource Guide.
- Videos New lightboard videos featuring the author, Michael Sullivan, that develop statistical concepts for students. New animated videos that explain concepts or tie material learned earlier in the course with the upcoming chapter or section. New Excel video solutions for any example in which Excel may be used to obtain statistical results.
- R Technology Guide Written by Patrick Murphy (nephew of the author) and Michael Sullivan, the R Technology Guide provides a chapter-by-chapter discussion of R commands needed for each topic.
- Putting It Together When students are learning statistics, often they struggle with seeing the big picture of how it all fits together. One of my goals is to help students learn not just the important concepts and methods of statistics but also how to put them together and see how the methods work together. On the inside front cover, you’ll see a pathway that provides a guide for students as they navigate through the process of learning statistics. The features and chapter organization in the sixth edition reinforce this important process. There are two categories of “Putting It Together.”
- Putting It Together Sections appear in Chapters 5, 9, 10, and 11. The problems in these sections are meant to help students identify the correct approach to solving a problem. Many exercises in these sections mix in inferential techniques from earlier sections. Plus, there are problems that require students to identify the inferential technique that may be used to answer the research objective (but no analysis is required). For example, see Problems 25 to 31 in Section 10.5.
- Putting It Together Problems appear throughout the text. The purpose of these problems is to tie concepts together and see the entire statistical process. For example, problems on hypothesis testing may require students to first identify the data collection method (such as observational study or designed experiment, the explanatory and response variables, the role of randomization, the role of control) prior to completing the data analysis.
- Student Activity Workbook The student activity workbook now contains an outline for a semester-long project and suggestions for how to use the StatCrunch survey tool to develop a survey that could result in a semester-long project. Plus, there are ten new activities included in the activity workbook along with suggested answers in the corresponding instructor’s guide.
- Retain Your Knowledge These problems occur periodically at the end of section exercises and are meant to assist students in retaining skills learned earlier in the course. This way, the material is fresh for the final exam.
- MyLab Technology Help Online homework problems that may be analyzed using statistical packages now have an updated technology help feature. Marked with a icon, this feature provides step-by-step instructions on how to obtain results using StatCrunch, TI-84 Plus/TI-84 Plus C, and Excel.
- Instructor Resource Guide Written by Michael Sullivan, the Instructor Resource Guide provides an overview of the chapter. It also details points to emphasize within each section and suggestions for presenting the material. In addition, the guide provides examples that may be used in the classroom. Many new examples have been added to this edition.
Key Chapter Content Changes
Chapter 1 Data Collection
Section 1.2 now includes a discussion of obtaining data through web scraping and how to obtain data from the Internet. Section 1.6 expands on the discussion of the placebo effect.
The material on stem-and-leaf plots was moved from Section 2.2 to Section 2.3.
Section 5.1 now distinguishes the Law of Large Numbers from the nonexistent Law of Averages. There is a new Section 5.6 on simulating probability experiments. This material is very helpful in allowing students to see the role of randomness in probability experiments. It also foreshadows topics such as sampling distributions and inference.
There is a new online section on combining random variables (Section 6.5). This includes topics such as the expected value and variance of the sum or difference of random variables.
Added a discussion on the normality condition for constructing confidence intervals for the population mean using Student’s t-distribution in Section 9.2.
Chapter 10 now contains optional sections on simulation methods for conducting inference. The organization of Chapter 10 allows for presenting simulation along with traditional inference, or simply presenting traditional inference. Should you decide to present only the traditional approach to inference (that is, through the normal model), simply cover Section 10.2 from the text. If you decide to present hypothesis testing using simulation, skip Section 10.2 in the text and cover Sections 10.2A and 10.2B (available in MyLab as pdfs). Section 10.3A (MyLab) presents hypothesis testing on a mean using simulation and bootstrapping. This section is optional and may be skipped without loss of continuity.
Chapter 11 has new optional sections on randomization methods. Section 11.1A (available in MyLab as a pdf) presents randomization tests for two independent proportions. If you choose to present randomization methods, we recommend presenting Section 11.1A prior to Section 11.1. Section 11.2A (MyLab) presents hypothesis tests on dependent means using bootstrapping. This section is optional and may be skipped without loss of continuity. Section 11.3A (MyLab) presents randomization tests for two independent means. We recommend covering this section prior to Section 11.3, if you choose to discuss this approach to hypothesis testing.
Chapter 14 has a new optional section on randomization. Section 14.1A (available in MyLab) presents randomization tests for the slope of the least-squares regression model. If you choose to cover this section, do so prior to Section 14.1.