Statistics for Business and Economics, 15/e (AE-Paperback)
暫譯: 商業與經濟統計學，第15版 (AE-平裝本)

David R. Anderson, Dennis J. Sweeney, James J. Cochran, Jeffrey D. Camm, Jeffrey W. Ohlmann, Michael J. Fry, Thomas A. Williams

Statistics for Business and Economics, 15/e (AE-Paperback)

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●ENGAGING CASE PROBLEMS PROVIDE ADDITIONAL OPPORTUNITIES TO PRACTICE SKILLS. Approximately 50 case problems in this edition provide students with opportunities to put what they’ve learned into action. Students work on more complex problems, analyze larger data sets and prepare managerial reports based on the results of their analyses.
●APPENDICES AND FIGURES HIGHLIGHT TODAY'S LATEST PROFESSIONAL SOFTWARE. All step-by-step instructions in this edition's software appendices and all textbook figures featuring software output now reference the latest versions of Excel, JMP® Student Edition and R (online only). Students gain important hands-on experience using these popular professional statistical analysis software tools.
●PROVEN, SYSTEMATIC APPROACH EMPHASIZES PROVEN METHODS AND APPLICATIONS. Students first develop a computational foundation and master the use of techniques before moving to statistical application and interpretation of the value of techniques. Methods Exercises at the end of each section stress computation and the use of formulas, while Application Exercises require students to apply what they know about statistics to real-world problems.
●SIGNIFICANTLY EXPANDED SOFTWARE SUPPORT FOR R PREPARES STUDENTS TO USE THIS IMPORTANT TOOL. Revised eBook and WebAssign digital chapter appendices now include relevant examples for easy reference. In addition, all scripts are updated to ensure compatibility with the most recent versions of R. The authors have also expanded the number of the scripts and .csv data sets to support the major chapter examples, application problems and cases.
●REORGANIZED AND EXPANDED CONTENT IN REGRESSION ANALYSIS AND MODEL BUILDING (CH. 16) CLARIFIES CONCEPTS. The authors have strengthened content throughout this chapter. For instance, the authors have added discussion that compares a regression model with a transformed dependent variable to a regression model using the untransformed dependent variable in the original units. In addition, this chapter includes a new example that illustrates the use of the Durbin-Watson statistic to test the presence of first-order autocorrelation.
●NEW PROBLEMS, CASES AND VIGNETTES KEEP CONTENT FRESH AND CURRENT. This edition includes more than 100 additional new problems as well as three new cases. In addition, the authors have added three new Statistic in Practice vignettes that reflect current challenges in statistics.
●UPDATED JMP CHAPTER APPENDICES REFLECT THE MOST RECENT VERSION OF JMP STATISTICAL SOFWARE. All JMP chapter appendices in both the printed or eBook incorporate changes to the most recent student version of JMP® -- JMP® Student Edition 16. You can be sure your students are able to work with the latest statistics digital support.
●TRUSTED TEAM OF DISTINGUISHED AUTHORS ENSURES THE MOST ACCURATE, PROVEN PRESENTATION. Prominent leaders and active consultants in business and statistics, this edition's authors Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann, David R. Anderson, Dennis J. Sweeney and Thomas A. Williams work seamlessly to deliver an accurate, real-world presentation of statistical concepts that you can trust for accuracy and comprehensive, engaging content.
●WEBASSIGN COURSE MANAGEMENT SOLUTION OFFERS A COMPREHENSIVE TEACHING TOOL FOR BUSINESS STATISTICS. This flexible and fully customizable platform puts powerful, time-saving tools in your hands. You can easily deploy assignments, instantly assess individual student and class performance and help struggling students master the course concepts. With WebAssign’s powerful digital platform and this edition's specific content, you can tailor your course with a wide range of assignment settings. Add your own questions and content and access student and course analytics and communication tools.
●NEW LEARNING OBJECTIVES DRAW STUDENT ATTENTION TO KEY CONCEPTS. This edition's new learning objectives, that now appear at the beginning of each chapter, detail and explain the key concepts that are covered in each chapter. These new learning objectives are also mapped to each problem so you can easily identify which problems are addressing specific individual learning objective for focused instruction.
●INTRIGUING EXAMPLES INCORPORATE REAL MEANINGFUL DATA. More than 100 new examples and exercises incorporate real data and reference timely sources to bring statistical concepts to life. The authors draw data from sources used by The Wall Street Journal, USA Today, Barron's and other leading publications. Using actual studies and applications, the authors present clear explanations and create exercises that demonstrate the many uses of statistics in business and economics today. In total, this edition provides more than 350 helpful examples and exercises.

商品描述(中文翻譯)

●引人入勝的案例問題提供額外的技能練習機會。本版中約有50個案例問題，讓學生有機會將所學知識付諸實踐。學生將處理更複雜的問題，分析更大的數據集，並根據分析結果準備管理報告。

●附錄和圖表突顯當今最新的專業軟體。本版的軟體附錄中的所有逐步指導和所有包含軟體輸出的教科書圖表現在都參考最新版本的Excel、JMP®學生版和R（僅限線上）。學生在使用這些流行的專業統計分析軟體工具時獲得重要的實作經驗。

●經驗證的系統性方法強調經過驗證的方法和應用。學生首先建立計算基礎並掌握技術的使用，然後再進入統計應用和技術價值的解釋。每個部分結尾的「方法練習」強調計算和公式的使用，而「應用練習」則要求學生將所學的統計知識應用於現實問題。

●顯著擴展的R軟體支援為學生準備使用這一重要工具。修訂的電子書和WebAssign數位章節附錄現在包含相關範例以便於參考。此外，所有腳本均已更新，以確保與最新版本的R相容。作者還擴大了腳本和.csv數據集的數量，以支援主要章節範例、應用問題和案例。

●重新組織和擴展的回歸分析和模型建立內容（第16章）澄清了概念。作者在本章中加強了內容。例如，作者增加了討論，將使用轉換的因變量的回歸模型與使用未轉換的因變量的回歸模型進行比較。此外，本章還包括一個新範例，說明如何使用Durbin-Watson統計量來檢測一階自相關的存在。

●新的問題、案例和小插曲使內容保持新鮮和當前。本版包含超過100個新的問題以及三個新案例。此外，作者還增加了三個新的「實踐中的統計」小插曲，反映當前統計學的挑戰。

●更新的JMP章節附錄反映JMP統計軟體的最新版本。所有JMP章節附錄在印刷版或電子書中均納入了對最新學生版本JMP® -- JMP®學生版16的更改。您可以確信您的學生能夠使用最新的統計數位支援。

●受信賴的傑出作者團隊確保最準確、經過驗證的呈現。這一版的作者Jeffrey D. Camm、James J. Cochran、Michael J. Fry、Jeffrey W. Ohlmann、David R. Anderson、Dennis J. Sweeney和Thomas A. Williams是商業和統計領域的知名領袖和活躍顧問，他們無縫合作，提供準確的現實世界統計概念呈現，您可以信賴其準確性和全面、引人入勝的內容。

●WebAssign課程管理解決方案提供全面的商業統計教學工具。這個靈活且完全可自訂的平台將強大的省時工具放在您的手中。您可以輕鬆部署作業，立即評估個別學生和班級的表現，並幫助有困難的學生掌握課程概念。利用WebAssign強大的數位平台和本版的特定內容，您可以根據各種作業設定來調整您的課程。添加您自己的問題和內容，並訪問學生和課程分析及溝通工具。

●新的學習目標吸引學生注意關鍵概念。本版的新學習目標現在出現在每章的開頭，詳細說明每章所涵蓋的關鍵概念。這些新的學習目標也與每個問題對應，讓您可以輕鬆識別哪些問題針對特定的個別學習目標進行集中教學。

●引人入勝的範例融入真實有意義的數據。超過100個新的範例和練習融入真實數據並參考及時來源，使統計概念生動呈現。作者從《華爾街日報》、《今日美國》、《巴倫周刊》和其他領先出版物中提取數據。通過使用實際研究和應用，作者提供清晰的解釋並創建練習，展示統計在當今商業和經濟中的多種用途。總體而言，本版提供超過350個有用的範例和練習。

作者簡介

Jeffrey D. Camm is the Inmar Presidential Chair of Analytics and Senior Associate Dean of Business Analytics programs in the School of Business at Wake Forest University. Born in Cincinnati, Ohio, he holds a B.S. from Xavier University (Ohio) and a Ph.D. from Clemson University. Prior to joining the faculty at Wake Forest, Dr. Camm served on the faculty of the University of Cincinnati. He has also been a visiting scholar at Stanford University and a visiting professor of business administration at the Tuck School of Business at Dartmouth College. Dr. Camm has published more than 45 papers in the general area of optimization applied to problems in operations management and marketing. He has published his research in Science, Management Science, Operations Research, Interfaces and other professional journals. Dr. Camm was named the Dornoff Fellow of Teaching Excellence at the University of Cincinnati and he was the recipient of the 2006 INFORMS Prize for the Teaching of Operations Research Practice. A firm believer in practicing what he preaches, he has served as an operations research consultant to numerous companies and government agencies. From 2005 to 2010, Dr. Camm served as editor-in-chief of INFORMS Journal of Applied Analytics (formerly Interfaces). In 2017, he was named an INFORMS fellow.

James J. Cochran is Professor of Applied Statistics, the Rogers-Spivey Faculty Fellow and Associate Dean for Faculty and Research at the University of Alabama. Born in Dayton, Ohio, he earned his B.S., M.S., and M.B.A. degrees from Wright State University and his Ph.D. from the University of Cincinnati. Dr. Cochran has served at The University of Alabama since 2014 and has been a visiting scholar at Stanford University, Universidad de Talca, the University of South Africa and Pole Universitaire Leonard de Vinci. Dr. Cochran has published more than 45 papers in the development and application of operations research and statistical methods. He has published his research in Management Science, The American Statistician, Communications in Statistics-Theory and Methods, Annals of Operations Research, European Journal of Operational Research, Journal of Combinatorial Optimization, INFORMS Journal of Applied Analytics and Statistics and Probability Letters. He was the 2008 recipient of the INFORMS Prize for the Teaching of Operations Research Practice and the 2010 recipient of the Mu Sigma Rho Statistical Education Award. Dr. Cochran was elected to the International Statistics Institute in 2005 and named a fellow of the American Statistical Association in 2011. He received the Founders Award in 2014 and the Karl E. Peace Award in 2015 from the American Statistical Association. In 2017 he received the American Statistical Association’s Waller Distinguished Teaching Career Award and was named a fellow of INFORMS. In 2018 he received the INFORMS President’s Award. A strong advocate for effective statistics and operations research education as a means of improving the quality of applications to real problems, Dr. Cochran has organized and chaired teaching workshops throughout the world.

Michael J. Fry is Professor of Operations, Business Analytics and Information Systems and Academic Director of the Center for Business Analytics in the Carl H. Lindner College of Business at the University of Cincinnati. Born in Killeen, Texas, he earned a B.S. from Texas A&M University and his M.S.E. and Ph.D. from the University of Michigan. He has been at the University of Cincinnati since 2002, where he was previously department head. Dr. Fry has been named a Lindner Research Fellow. He has also been a visiting professor at the Samuel Curtis Johnson Graduate School of Management at Cornell University and the Sauder School of Business at the University of British Columbia. Dr. Fry has published more than 25 research papers in journals such as Operations Research, M&SOM, Transportation Science, Naval Research Logistics, IISE Transactions, Critical Care Medicine and INFORMS Journal of Applied Analytics (formerly Interfaces). His research interests are in applying quantitative management methods to the areas of supply chain analytics, sports analytics and public-policy operations. He has worked with many organizations for his research, including Dell, Inc., Starbucks Coffee Company, Great American Insurance Group, the Cincinnati Fire Department, the State of Ohio Election Commission, the Cincinnati Bengals and the Cincinnati Zoo and Botanical Garden. Dr. Fry was named a finalist for the Daniel H. Wagner Prize for Excellence in Operations Research Practice, and he has been recognized for both his research and teaching excellence at the University of Cincinnati.

Jeffrey W. Ohlmann is Associate Professor of Management Sciences and Huneke Research Fellow in the Tippie College of Business at the University of Iowa. Born in Valentine, Nebraska, he earned a B.S. from the University of Nebraska, and his M.S. and Ph.D. from the University of Michigan. He has been at the University of Iowa since 2003. Dr. Ohlmann’s research on the modeling and solution of decision-making problems has produced more than two dozen research papers in journals such as Operations Research, Mathematics of Operations Research, INFORMS Journal on Computing, Transportation Science, the European Journal of Operational Research and INFORMS Journal of Applied Analytics (formerly Interfaces). He has collaborated with companies such as Transfreight, LeanCor, Cargill, the Hamilton County Board of Elections as well as three National Football League franchises. Because of the relevance of his work to industry, he was bestowed the George B. Dantzig Dissertation Award and was recognized as a finalist for the Daniel H. Wagner Prize for Excellence in Operations Research Practice.

David R. Anderson is a leading author and professor emeritus of quantitative analysis in the College of Business Administration at the University of Cincinnati. Dr. Anderson has served as head of the Department of Quantitative Analysis and Operations Management and as associate dean of the College of Business Administration. He was also coordinator of the college’s first executive program. In addition to introductory statistics for business students, Dr. Anderson taught graduate-level courses in regression analysis, multivariate analysis and management science. He also taught statistical courses at the Department of Labor in Washington, D.C. Dr. Anderson has received numerous honors for excellence in teaching and service to student organizations. He is the co-author of ten well-respected textbooks related to decision sciences, and he actively consults with businesses in the areas of sampling and statistical methods. Born in Grand Forks, North Dakota, Dr. Anderson earned his B.S., M.S. and Ph.D. degrees from Purdue University.

Dennis J. Sweeney is professor emeritus of quantitative analysis and founder of the Center for Productivity Improvement at the University of Cincinnati. Born in Des Moines, Iowa, he earned a B.S.B.A. degree from Drake University and his M.B.A. and D.B.A. degrees from Indiana University, where he was an NDEA fellow. Dr. Sweeney has worked in the management science group at Procter & Gamble and has been a visiting professor at Duke University. He also served as head of the Department of Quantitative Analysis and served four years as associate dean of the College of Business Administration at the University of Cincinnati. Dr. Sweeney has published more than 30 articles and monographs in the area of management science and statistics. The National Science Foundation, IBM, Procter & Gamble, Federated Department Stores, Kroger and Cincinnati Gas & Electric have funded his research, which has been published in journals such as Management Science, Operations Research, Mathematical Programming and Decision Sciences. Dr. Sweeney has co-authored ten textbooks in the areas of statistics, management science, linear programming and production and operations management.

作者簡介(中文翻譯)

Jeffrey D. Camm 是維克森林大學商學院的 Inmar 總統分析椅及商業分析計畫的高級副院長。他出生於俄亥俄州辛辛那提，擁有來自 Xavier University（俄亥俄州）的學士學位及來自 Clemson University 的博士學位。在加入維克森林大學的教職之前，Camm 博士曾在辛辛那提大學任教。他也曾擔任史丹佛大學的訪問學者，以及達特茅斯學院 Tuck 商學院的商業管理訪問教授。Camm 博士在應用於運營管理和行銷問題的優化領域發表了超過 45 篇論文。他的研究發表於《Science》、《Management Science》、《Operations Research》、《Interfaces》及其他專業期刊。Camm 博士曾獲得辛辛那提大學的 Dornoff 教學卓越獎，並於 2006 年獲得 INFORMS 運營研究實踐教學獎。他堅信實踐所教，曾擔任多家企業和政府機構的運營研究顧問。從 2005 年到 2010 年，Camm 博士擔任 INFORMS Journal of Applied Analytics（前身為 Interfaces）的主編。2017 年，他被任命為 INFORMS 會士。


James J. Cochran 是阿拉巴馬大學應用統計學教授、Rogers-Spivey 教職研究員及教職與研究副院長。他出生於俄亥俄州代頓，獲得 Wright State University 的學士、碩士及工商管理碩士學位，並在辛辛那提大學獲得博士學位。Cochran 博士自 2014 年以來在阿拉巴馬大學任教，並曾擔任史丹佛大學、塔爾卡大學、南非大學及倫納德·德·文西大學的訪問學者。Cochran 博士在運營研究和統計方法的開發與應用方面發表了超過 45 篇論文。他的研究發表於《Management Science》、《The American Statistician》、《Communications in Statistics-Theory and Methods》、《Annals of Operations Research》、《European Journal of Operational Research》、《Journal of Combinatorial Optimization》、《INFORMS Journal of Applied Analytics》及《Statistics and Probability Letters》。他於 2008 年獲得 INFORMS 運營研究實踐教學獎，並於 2010 年獲得 Mu Sigma Rho 統計教育獎。Cochran 博士於 2005 年當選為國際統計學會會員，並於 2011 年被任命為美國統計學會會士。他於 2014 年獲得創始人獎，並於 2015 年獲得 Karl E. Peace 獎。2017 年，他獲得美國統計學會的 Waller 傑出教學生涯獎，並被任命為 INFORMS 會士。2018 年，他獲得 INFORMS 會長獎。Cochran 博士強烈倡導有效的統計和運營研究教育，以改善對實際問題的應用質量，並在全球範圍內組織和主持教學研討會。


Michael J. Fry 是辛辛那提大學 Carl H. Lindner 商學院的運營、商業分析和資訊系統教授及商業分析中心的學術主任。他出生於德克薩斯州基林，獲得德克薩斯 A&M 大學的學士學位，並在密西根大學獲得碩士及博士學位。自 2002 年以來，他一直在辛辛那提大學任教，曾擔任系主任。Fry 博士被任命為 Lindner 研究員。他也曾擔任康奈爾大學 Samuel Curtis Johnson 研究生管理學院及不列顛哥倫比亞大學 Sauder 商學院的訪問教授。Fry 博士在《Operations Research》、《M&SOM》、《Transportation Science》、《Naval Research Logistics》、《IISE Transactions》、《Critical Care Medicine》和《INFORMS Journal of Applied Analytics》（前身為 Interfaces）等期刊上發表了超過 25 篇研究論文。他的研究興趣在於將定量管理方法應用於供應鏈分析、體育分析和公共政策運營等領域。他曾與許多組織合作進行研究，包括 Dell, Inc.、星巴克咖啡公司、大美國保險集團、辛辛那提消防局、俄亥俄州選舉委員會、辛辛那提猛虎隊和辛辛那提動物園及植物園。Fry 博士曾被提名為 Daniel H. Wagner 運營研究實踐卓越獎的決賽入圍者，並因其在辛辛那提大學的研究和教學卓越而受到認可。


Jeffrey W. Ohlmann 是愛荷華大學 Tippie 商學院的管理科學副教授及 Huneke 研究員。他出生於內布拉斯加州瓦倫丁，獲得內布拉斯加大學的學士學位，並在密西根大學獲得碩士及博士學位。自 2003 年以來，他一直在愛荷華大學任教。Ohlmann 博士在決策問題建模和解決方面的研究已在《Operations Research》、《Mathematics of Operations Research》、《INFORMS Journal on Computing》、《Transportation Science》、《European Journal of Operational Research》和《INFORMS Journal of Applied Analytics》（前身為 Interfaces）等期刊上發表了超過二十篇研究論文。他曾與 Transfreight、LeanCor、Cargill、漢密爾頓縣選舉委員會及三個國家橄欖球聯盟特許經營隊合作。由於他的工作與行業的相關性，他獲得了 George B. Dantzig 論文獎，並被提名為 Daniel H. Wagner 運營研究實踐卓越獎的決賽入圍者。


David R. Anderson 是辛辛那提大學商業管理學院的著名作者及定量分析名譽教授。Anderson 博士曾擔任定量分析與運營管理系主任及商業管理學院的副院長。他還是該學院首個高管計畫的協調員。除了為商業學生教授入門統計學外，Anderson 博士還教授回歸分析、多變量分析和管理科學的研究生課程。他還在華盛頓特區的勞工部教授統計課程。Anderson 博士因其教學和對學生組織的服務而獲得了多項榮譽。他是十本與決策科學相關的受人尊敬的教科書的共同作者，並在抽樣和統計方法領域積極為企業提供諮詢服務。他出生於北達科他州的格蘭德福克斯，並在普渡大學獲得學士、碩士和博士學位。


Dennis J. Sweeney 是辛辛那提大學定量分析名譽教授及生產力改善中心的創始人。他出生於愛荷華州德莫因，獲得德雷克大學的商業管理學士學位，並在印第安納大學獲得工商管理碩士及博士學位，當時他是 NDEA 獎學金獲得者。Sweeney 博士曾在寶潔公司的管理科學小組工作，並曾擔任杜克大學的訪問教授。他還擔任定量分析系主任，並在辛辛那提大學的商業管理學院擔任副院長四年。Sweeney 博士在管理科學和統計領域發表了超過 30 篇文章和專著。他的研究得到了國家科學基金會、IBM、寶潔公司、聯合百貨、克羅格和辛辛那提燃氣與電力公司的資助，並發表於《Management Science》、《Operations Research》、《Mathematical Programming》和《Decision Sciences》等期刊。Sweeney 博士在統計、管理科學、線性規劃及生產與運營管理等領域共同編寫了十本教科書。

目錄大綱

1. Data and Statistics.
2. Descriptive Statistics: Tabular and Graphical Displays.
3. Descriptive Statistics: Numerical Measures.
4. Introduction to Probability.
5. Discrete Probability Distributions.
6. Continuous Probability Distributions.
7. Sampling and Sampling Distributions.
8. Interval Estimation.
9. Hypothesis Tests.
10. Inference about Means and Proportions with Two Populations.
11. Inferences about Population Variances.
12. Comparing Multiple Proportions, Test of Independence and Goodness of Fit.
13. Experimental Design and Analysis of Variance.
14. Simple Linear Regression.
15. Multiple Regression.
16. Regression Analysis: Model Building.
17. Time Series Analysis and Forecasting.
18. Nonparametric Methods.
19. Decision Analysis.
20. Index Numbers.
21. Statistical Methods for Quality Control.
22. Sample Survey.
Appendix A. References and Bibliography.
Appendix B. Tables.
Appendix C. Summation Notation.
Appendix D. Microsoft Excel and Tools for Statistical Analysis.
Appendix E. Computing p-Values Using JMP and Excel.
Appendix F: Microsoft Excel Online and Tools for Statistical Analysis.
Appendix G: Solutions to Even-Numbered Exercises (Cengage eBook).

目錄大綱(中文翻譯)

1. Data and Statistics.

2. Descriptive Statistics: Tabular and Graphical Displays.

3. Descriptive Statistics: Numerical Measures.

4. Introduction to Probability.

5. Discrete Probability Distributions.

6. Continuous Probability Distributions.

7. Sampling and Sampling Distributions.

8. Interval Estimation.

9. Hypothesis Tests.

10. Inference about Means and Proportions with Two Populations.

11. Inferences about Population Variances.

12. Comparing Multiple Proportions, Test of Independence and Goodness of Fit.

13. Experimental Design and Analysis of Variance.

14. Simple Linear Regression.

15. Multiple Regression.

16. Regression Analysis: Model Building.

17. Time Series Analysis and Forecasting.

18. Nonparametric Methods.

19. Decision Analysis.

20. Index Numbers.

21. Statistical Methods for Quality Control.

22. Sample Survey.

Appendix A. References and Bibliography.

Appendix B. Tables.

Appendix C. Summation Notation.

Appendix D. Microsoft Excel and Tools for Statistical Analysis.

Appendix E. Computing p-Values Using JMP and Excel.

Appendix F: Microsoft Excel Online and Tools for Statistical Analysis.

Appendix G: Solutions to Even-Numbered Exercises (Cengage eBook).

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