In ** Statistically Speaking, **thirty-two video lessons are uniquely embedded in a special edition of W.H. Freeman’s StatsPORTAL. Instructors select from one of two textbook options, and students purchase an eBook/StatsPORTAL token.

All of the learning assets listed below are contained in this course and provide learners with multiple ways to learn statistical concepts:

- Thirty-two, 30-minute videos.
*Student Study Guide*designed to assist the student’s learning experience. It includes*Selected Solutions*explanations of crucial concepts with step-by-step models of statistical techniques.*StatTutor Tutorials*tied directly to the textbook, containing videos, applets, and animations.- Statistical applets to help students master key concepts.
*CrunchIt!*statistical software offering the basic statistical routines covered in the introductory courses and more.*Stats@Work Simulations*put the student in the role of consultant, helping them better understand statistics within the context of real-life scenarios.*EESEE Case Studies*developed by The Ohio State University Statistics Department are current real examples with real data. Each one is built to cause students think critically about statistical issues.*Podcast Chapter Summary*provides students with a downloadable MP3 version of chapter summaries.- Data sets are available in ASCII, Excel, JMP, Minitab, TI, SPSS, and S-Plus.
- Online tutoring with
*SmarThinking*is available as a homework resource for students, staffed with professional educators.

The first choice of recommended textbook is Basic Practice of Statistics, 6th edition, by Moore, Notz, and Fligner and published by W. H. Freeman. The second choice is The Introduction to the Practice of Statistics, 7th edition, by Moore, McCabe, and Craig and published by W. H. Freeman.

#### Lesson Titles and Descriptions

#### 1. Introduction to Statistics

#### 2. Displaying Data

One of the first things scientists look at is the data to determine how it is distributed. Researchers report statistical data by using various types of graphical displays. We all have seen examples of this on a daily basis. One of these examples is the colored map we often see on the evening news or the Internet of the United States summarizing voter counts and surveys. With the seemingly endless public-official election cycle, most people know whether they reside in a “red” or “blue” state.

This lesson will explain and demonstrate how to create these displays and how to interpret the meaning they convey

#### 3. Describing Distributions

#### 4. Normal Distributions

#### 5. Scatterplots & Correlation

#### 6. Regression

One common association that may appear on a scatterplot occurs when the response variable y changes at the same rate as the explanatory variable x. When this happens, the scatterplot of the association reveals data points distributed around what appears to be straight line, something known as a linear relationship. In this lesson, you will take a closer look at linear relationships, and discover how statisticians use them to make predictions through a powerful statistical method called regression.

#### 7. Two-Way Tables

#### 8. Producing Data: Sampling

#### 9. Producing Data: Experimentation

products are better than ever…” appear frequently in news releases and advertisements. Many of these studies claim results based on experiments. But what constitutes a proper experiment? How do we know the results are reliable and have real meaning? How does one design an experiment to ensure that the research objective is properly addressed? Resolving these issues are part of the statistician’s role in research.This lesson describes how researchers collect data by designing and conducting experiments. First, you will learn how poorly designed studies can lead to flawed data with very little meaning. You will then see how researchers turn to statisticians to design experiments that account for any factors that could mask or confound any real, meaningful result. Proper experimental design is a powerful tool for researchers to use in seeking a greater understanding of the world around us.

#### 10. More on Experimentation

#### 11. Introduction to Inference

#### 12. Probability

#### 13. Sampling Distributions & the Central Limit Theorem

#### 14. Confidence Intervals

most commonly reported statistical results. Whether it involves who will win the next race for governor or what percentage of the population favors off shore drilling, the polls invariably report a percentage plus or minus some number. That range is the confidence interval, the subject of this lesson. It tells viewers the range of accuracy for that poll; in other words, by how much the results may be off. If the numbers in a poll are close and fall within this range, you know that the race or debate is not over.This lesson gives you the statistical background to fully understand how these numbers are calculated, what the numbers mean and how they help statisticians to be confident about the results they report. The lesson builds on what you have learned about how to use sampling data to infer population parameters, such as the mean. Mastering these concepts will deepen your understanding of inference and prepare you to learn how to conduct more sophisticated tests in future lessons.

#### 15. Tests of Significance

#### 16. Course Lesson Title

#### 17. Cautions About Inference

#### 18. Comparing Two Means

#### 19. Inference for Proportions

#### 20. Comparing Two Proportions

#### 21. Choosing Inference Procedures

#### 22. Chi-Square Tests

#### 23. Inference for Regression

#### 24. Multiple Regression: Building the Model

#### 25. Multiple Regression: Refining the Model

*t*statistical test to evaluate the individual slope coefficients. This is a test with which you are already quite familiar. In this lesson, you’ll learn how the

*t*test gives researchers a way to identify which predictors to keep and which to remove from the regression model. The lesson features a real life example of how scientists use these refinement techniques to keep the electric power grid going and also to predict how many solar panels customers need to produce their own electricity. Remember that the purpose of multiple regression is to accurately predict the value of a response variable based on the value of one or more explanatory, or predictor, variables. In this lesson, you will also learn how to apply the principles of inference to predicted values.

#### 26. Logistic Regression

#### 27. One-Way ANOVA

#### 28. Contrasts: Comparing Means

*F*test gives us valuable information about the groups under study by telling us whether or not the means of the groups are equal. As powerful as ANOVA is, however, it does not tell us which group is different, by how much, and whether the difference is significant. To discover that information, we need to compare the means of the groups.In this lesson, you will learn how statisticians use computer software to simultaneously examine the differences among the means of three or more groups. As you learned in the ANOVA lesson, we cannot use separate two-sample

*t*tests to compare these multiple groups because that test does not account for the fact that the groups are being evaluated together at the same time. In this lesson, you will learn how statisticians evaluate the significance and magnitude of the unequal means identified by a significant ANOVA test.

#### 29. Two-Way ANOVA

*F*test using computer software to make the calculations. While it makes for a more complex analysis, this powerful technique can allow researchers to investigate the simultaneous impact of two conditions on a response variable.

#### 30. Bootstrap Methods & Permutation Tests

#### 31. Nonparametric Tests

#### 32. Statistical Process Control

#### National Academic Advisory Team

Diane L. Benner, M.S., Associate Dean, Mathematics, Science, & Allied Health Division, Harrisburg Area Community College

Keith Bower, M.S., Statistician, www.KeithBower.com

Matthew A. Carlton, Ph.D., Associate Professor, Department of Statistics, California Polytechnic State University, San Luis Obispo

Bruce J. Collings, Ph.D., Professor of Statistics, Department of Statistics, Brigham Young University

Patti B. Collings, M.S., Assistant Teaching Professor, Department of Statistics, Brigham Young University

Mary Ellen Davis, M.S., Associate Professor, Mathematics, Computer Science, Engineering, Georgia Perimeter College, Clarkston Campus

Robert L. Gould, Ph.D., Academic Administrator & Undergraduate Vice-Chair, Department of Statistics, University of California, Los Angeles

Karen McGaughey, Ph.D., Assistant Professor, Department of Statistics, California Polytechnic State University, San Luis Obispo

Mary Mortlock, M.S., AP Statistics Teacher, The Harker School

Kathy Mowers, M.A.T., Mathematics Professor & Coordinator, Owensboro Community & Technical College

Linda Myers, Ph.D., Professor, Mathematics, Computer Science, Harrisburg Area Community College

Robert L. Raymond, Ph.D., Professor Emeritus, Computer & Information Sciences Department, University of St. Thomas, Minnesota

Daren Starnes, Master Teacher, The Lawrenceville School

#### On-Camera Experts

Sandeep Arora, Transmission Engineer, California ISO

Tim Barnett, Ph.D., Climatologist, Scripps Institution of Oceanography, University of California, San Diego

Keith M. Bower, M.S., Statistician

Allan Brandt, Ph.D., Professor of the History of Science, Harvard University

Mike Bullock, M.B.A., Managing Master Black Belt, Six SigmaTM, Quest Diagnostics

Lucius D. Bunton III, J.D., Judge, Western District of Texas, U. S. District Court

Jeff Burtt, Field Office Supervisor, Times/Bloomberg Poll

C. Wayne Callaway, M.D., P.C., Endocrinologist

Karen Chaudiere, M.B.A., Six SigmaTM Black Belt, Quest Diagnostics

Paul Chodas, Ph.D., Principal Engineer, Near-Earth Object Program, NASA Jet Propulsion Laboratory

Daniel Clegg, M.D., Professor of Rheumatology, University of Utah

Bruce Codley, Statistician, Risk Assessment, Bell Communications Research

Bruce Jay Collings, Ph.D., Professor of Statistics, Brigham Young University

Tim Crane, Purchasing Manager, REC Solar

Jill Darling, Associate Director, Times/Bloomberg Poll

Gianluca Del Rossi, Ph.D., Professor of Sports Medicine, University of Miami

Dr. W. Edwards Deming, Management Theorist & Statistician

Jim Detmers, Vice President, Operations, California ISO

George Dickison, M.S., Director, Natural Resource Center, National Park Service

Lou Dieter

Nolan Doesken, M.S., Colorado State Climatologist, Colorado State University

Bonnie J. Dunbar, Ph.D., President & CEO, Seattle Museum of Flight

Dennis Eggett, Ph.D., Professor, Statistics Department, Brigham Young University

Gregg Fishman, Public Information Officer, California ISO

Eric Frank, Ph.D., Dean, V.P. of Academic Affairs, Occidental College

Lawrence Garfinkle, Director of Cancer Prevention, American Cancer Society

Dennis Gaushell, Load Forecasting Analyst, California ISO

Spencer Guthrie, Ph.D., Assistant Professor, Brigham Young University

Dana Hall, Statistician, San Diego State University

Linnea S. Hall, Ph.D., Executive Director, Western Foundation of Vertebrate Zoology

James Halverson, M.D., Internist, Ojai Valley Medical Group

Stanley Heshka, Ph.D., St. Luke’s – Roosevelt Hospital

John Hostetter, Design Engineer, REC Solar

Sylvia Hurtado, Ph.D., Director, Higher Education Research Institute, University of California, Los Angeles

Jim Jackson

Gary LaFree, Ph.D., Statistician, University of New Mexico

Wing Lam, Co-founder, Wahoo’s Fish Taco

Eric Larson, M.D., Executive Director Group Health Cooperative

Michael Lind, Area Sales Manager, REC Solar

Jim Loftis, Ph.D., Civil & Environmental Engineering, Colorado State University

Raúl E. López, Ph.D., Research Meteorologist, National Severe Storms Laboratory, NOAA

Carol Mansfield, Ph.D., Senior Economist, RTI International

Amy Miller Bohn, M.D., Family Physician, University of Michigan Health System

Ethan Miller, Director of Implementation, REC Solar

Irwin Miller, Statistician

Cheryl Millet, Supervisor, Specimen Management, Quest Diagnostics

Connie Moore, Supervisor, AT&T

Jennifer Moore, M.S., Graduate Assistant/Researcher, Colorado State University

Philip R. Nader, M.D., Professor of Pediatrics, University of California, San Diego Medical Center

Yukie Nishinaga, Marketing Manager,REC Solar

Greg O’Neill, M.S., Chief, Lakewood Office, U.S. Geological Survey

Jason Oppler, Inside Sales Manager, REC Solar

Richard Overholt, M.D., Clinical Professor of Surgery, Tufts College Medical School

Bruce Peacock, Ph.D., Economist, National Park Service

Matt Perez, Special Agent FBI

David Pierce, Ph.D., Climatologist, Scripps Institution of Oceanography

Susan Pinkus, Director, Times/Bloomberg Poll

John Pryor, M.A., Director CIRP, University of California, Los Angeles Higher Education Research Institute

Domenic Reda, Ph.D., Director/Cooperative Studies Program, Department of Veterans’ Affairs

Shane Reese, Ph.D., Associate Professor of Statistics, Brigham Young University

Maile Rogers, M.S., Instructor, Brigham Young University

Forest Rohwer, Ph.D., Professor of Molecular Biology, San Diego State University

Stuart Sandin, Ph.D., Coral Reef Ecologist, Scripps Institution of Oceanography

Frank Scoblete, Author, “Golden Touch Dice Control Revolution”

Jason Shaw, Human Resource Administrator, REC Solar

Zack Shelley, M.S., Program Director, Big Thompson Watershed Forum

Joseph Signorile, Ph.D., Professor of Physiology, University of Miami

Jennifer Smith, Ph.D., Assistant Professor, Scripps Institution of Oceanography

Steven Smriga, Scripps Institution of Oceanography, University of California, San Diego

Michael Tamada, Director of Institutional Research, Occidental College

Carl Thelander, M.S., Chief Executive Officer, Bio Resource Consultants

Dennis Tolley, Ph.D., Professor of Statistics, Brigham Young University

Li Wang, Ph.D., Statistician, R & D Service, Veterans Administration Health Services

Linda Wegley, Graduate Student, San Diego State University

Ernest Wynder, M.D., Past President, American Health Foundation

Donald Yeomans, Ph.D., Manager, Near-Earth Object Program, NASA-JPL

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