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Business Statistics, Global Edition 4th Edition



Business Statistics, Global Edition 4th Edition PDF

Author: Norean Sharpe, Richard De Veaux

Publisher: Pearson

Genres:

Publish Date: March 4, 2021

ISBN-10: 1292269316

Pages: Pages

File Type: PDF

Language: English

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Book Preface

The question that should motivate a business student’s study of statistics should be “Even without perfect information, how can I make better decisions?”1 As entre-preneurs and consultants, we know that in today’s data-rich environment, knowl-edge of statistics is essential to survive and thrive in the business world. But, as educators, we’ve seen a disconnect between the way business statistics is tradition-ally taught and the way it should be used in making business decisions. In Busi-ness Statistics, we try to narrow the gap between theory and practice by presenting relevant statistical methods that will empower business students to make effective, data-informed decisions.
Of course, students should come away from their statistics course knowing how to think statistically and how to apply statistics methods with modern technol-ogy. But they must also be able to communicate their analyses effectively to others. When asked about statistics education, a group of CEOs from Fortune 500 compa-nies recently said that although they were satisfied with the technical competence of students who had studied statistics, they found the students’ ability to communi-cate their findings to be woefully inadequate.
Our Plan, Do, Report rubric provides a structure for solving business problems that mimics the correct application of statistics to solving real business problems. Unlike many other authors, we emphasize the often neglected thinking (Plan) and communication (Report) steps in problem solving in addition to the methodol-ogy (Do). This approach requires up-to-date, real-world examples and data. So we constantly strive to illustrate our lessons with current business issues and examples.
What’s New in This Edition?

We’ve been delighted with the reaction to previous editions of Business Statistics. We’ve made some changes to the organization of the fourth edition to help students focus on the essentials and think about the data-rich world they will find in the workplace. And, of course, we continue to update examples and exercises so that the story we tell is always tied to the ways statistics informs modern business practice.

• Recent data. We teach with real data whenever possible, so we’ve updated data throughout the book. New examples ref lect current stories in the news and recent economic and business events. When a historical dataset is espe-cially good at illuminating a pedagogical point, we have, from time to time, chosen pedagogy over recency.
• Improved organization. We have retained our “data first” presentation of topics because we find that it provides students with both motivation and a foundation in real business decisions on which to build an understanding.
• Chapters 1–4 have been streamlined to cover collecting, displaying, summarizing, and understanding data in four chapters. We find that this provides students with a solid foundation to launch their study of prob-ability and statistics.
• Chapters 5–7 introduce students to randomness and probability models. We’ve moved the discussion of probability trees and Bayes’ rule into these chapters.
• Chapters 8 and 9 cover data collection by survey and by designed exper-iments. New discussions here address technology-enabled sampling, online data, and Big Data. We’ve moved the discussion of experiments up front because of the increased importance of online testing, but we’ve moved the analysis of such designs (ANOVA), which many instructors find difficult to cover in a first course, to the online Chapter 25.

• Chapters 10–15 cover inference for both proportions and means. We introduce inference by discussing proportions because most students are better acquainted with proportions reported in surveys and news stories. However, this edition ties in the discussion of means immediately so students can appreciate that the reasoning of inference is the same in a variety of contexts. We’ve added an optional discussion of bootstrap-ping. This may help students’ intuition about inference as well as pro-viding a relatively new modern method.
• Chapters 16–19 cover regression-based models for decision making.
• Chapter 20 discusses time series methods.
• Chapter 21 is a newly expanded discussion of data mining and Big Data.
• Chapters 22–24 discuss special topics that can be selected according to the needs of the course and the preferences of the instructor.

• Streamlined design. Our goal has always been a readable text. This edition sports a new design that clarifies the purpose of each text element. The major theme of each chapter is linear and easy to follow without distraction. Sup-porting material is clearly boxed and shaded, so students know where to focus their study efforts.
• Enhanced Technology Help. We’ve updated Technology Help (now called Tech Support) in almost every chapter.
• Updated examples to reflect the changing world. The time since our last revision has seen marked changes in the U.S. and world economies. This has required us to update many of our examples. Our selection of course content ref lects the wisdom of the GAISE 2016 report adopted by the American Sta-tistical Association as a standard for introductory statistics teaching. Our “In Practice” elements have all been re-structured to ref lect real-world business challenges. The result is a text that is realistic and useful.
• Increased focus on core material. Statistics in practice means making smart decisions based on data. Students need to know the methods, how to apply them, and the assumptions and conditions that make them work. We’ve tight-ened our discussions to get students there as quickly as possible, focusing increasingly on the central ideas and core material.

Our Approach

Statistical Thinking
For all of our improvements, examples, and updates in this edition of Business Sta-tistics we haven’t lost sight of our original mission—writing a modern business sta-tistics text that addresses the importance of statistical thinking in making business decisions and that acknowledges how Statistics is actually used in business.
Statistics is practiced with technology, and this insight informs everything from our choice of forms for equations (favoring intuitive forms over calculation forms) to our extensive use of real data. But most important, understanding the value of technology allows us to focus on teaching statistical thinking rather than calculation. The questions that motivate each of our hundreds of examples are not “How do you find the answer?” but “How do you think about the answer?”; “How does it help you make a better decision?”; and “How can you best communicate your decision?” Our redesigned “In Practice” elements in each chapter have been recast as conversations between managers and analysts to emphasize the business relevance of each method and its importance in making good business decisions.

Our focus on statistical thinking ties the chapters of the book together. An introductory Business Statistics course covers an overwhelming number of new terms, concepts, and methods, and it is vital that students see their central core: how we can understand more about the world and make better decisions by under-standing what the data tell us. From this perspective, it is easy to see that the pat-terns we look for in graphs are the same as those we think about when we prepare to make inferences. And it is easy to see that the many ways to draw inferences from data are several applications of the same core concepts. It follows naturally that when we extend these basic ideas into more complex (and even more realistic) situations, the same basic reasoning is still at the core of our analyses.
Our Goal: Read This Book!
The best textbook in the world is of little value if it isn’t read. Here are some of the ways we made Business Statistics more approachable:

• Readability. We strive for a conversational, approachable style, and we intro-duce anecdotes to maintain interest. Instructors report (to their amazement) that their students read ahead of their assignments voluntarily. Students tell us (to their amazement) that they actually enjoy the book. In this edition, we’ve focused our discussions even more clearly on the central ideas we want to convey.
• Focus on assumptions and conditions. More than any other textbook, Business Statistics emphasizes the need to verify assumptions when using statistical pro-cedures. We reiterate this focus throughout the examples and exercises. We make every effort to provide templates that reinforce the practice of checking these assumptions and conditions, rather than rushing through the computa-tions. Business decisions have consequences. Blind calculations open the door to errors that could easily be avoided by taking the time to graph the data, check assumptions and conditions, and then check again that the results and residuals make sense.
• Emphasis on graphing and exploring data. Our consistent emphasis on the importance of displaying data is evident from the first chapters on understand-ing data to the sophisticated model-building chapters at the end. Examples often illustrate the value of examining data graphically, and the exercises rein-force this. Good graphics reveal structures, patterns, and occasional anomalies that could otherwise go unnoticed. These patterns often raise new questions and inform both the path of a resulting statistical analysis and the business decisions. Hundreds of new graphics found throughout the book demonstrate that the simple structures that underlie even the most sophisticated statistical inferences are the same ones we look for in the simplest examples. This helps tie the concepts of the book together to tell a coherent story.
• Consistency. We work hard to avoid the “do what we say, not what we do” trap. Having taught the importance of plotting data and checking assump-tions and conditions, we are careful to model that behavior throughout the book. (Check the exercises in the chapters on multiple regression or time series and you’ll find us still requiring and demonstrating the plots and checks that were introduced in the early chapters.) This consistency helps reinforce these fundamental principles and provides a familiar foundation for the more sophisticated topics.
• The need to read. In this book, important concepts, definitions, and sample solutions are not always set aside in boxes. The book needs to be read, so we’ve tried to make the reading experience enjoyable. The common approach of skimming for definitions or starting with the exercises and looking up examples just won’t work here. (It never did work as a way to learn about and understand statistics.)

Coverage

The topics covered in a Business Statistics course are generally mandated by our students’ needs in their studies and in their future professions. But the order of these topics and the relative emphasis given to each is not well established. Busi-ness Statistics presents some topics sooner or later than other texts. Although many chapters can be taught in a different order, we urge you to consider the order we have chosen.
We’ve been guided in the order of topics by the fundamental goal of design-ing a coherent course in which concepts and methods fit together to provide a new understanding of how reasoning with data can uncover new and important truths. Each new topic should fit into the growing structure of understanding that students develop throughout the course. For example, we teach inference concepts with proportions first and then with means. Most people have a wider experience with proportions, seeing them in polls and advertising. And by starting with pro-portions, we can teach inference with the Normal model and then introduce infer-ence for means with the Student’s t-distribution.
We introduce the concepts of association, correlation, and regression early in Business Statistics. Our experience in the classroom shows that introducing these fundamental ideas early makes statistics useful and relevant even at the beginning of the course. By Chapter 4, students can discuss relationships among variables in a meaningful way. Later in the semester, when we discuss inference, it is natural and relatively easy to build on the fundamental concepts learned earlier and enhance them with inferential methods.
GAISE Report
We’ve been guided in our choice of what to emphasize by the 2016 GAISE (Guidelines for Assessment and Instruction in Statistics Education) report, which emerged from extensive studies of how students best learn Statistics (www.amstat .org/asa/files/pdfs/GAISE/GaiseCollege_Full.pdf). The GAISE report was extensively revised in 2016 to ref lect the evolution of technology and new wisdom about teaching statistics. The new recommendations have been officially adopted and recommended by the American Statistical Association and urge (among other detailed suggestions) that statistics education should:

1. Teach statistical thinking.
2. Focus on conceptual understanding.
3. Integrate real data with a context and a purpose.
4. Foster active learning.
5. Use technology to explore concepts and analyze data.
6. Use assessments to improve and evaluate student learning. In this sense, this book is thoroughly modern.


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