Statistics for Business and Economics (15版)
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內容描述:
●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.
內容描述(中文翻譯)
●引人入勝的案例問題提供了額外的練習機會。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。
●附錄和圖表突出了當今最新的專業軟件。本版的軟件附錄中的逐步指導和所有包含軟件輸出的教科書圖表現在引用的是最新版本的Excel、JMP®學生版和R（僅限線上）。學生通過使用這些流行的專業統計分析軟件工具獲得重要的實踐經驗。
●經過驗證的系統化方法強調經過驗證的方法和應用。學生首先建立計算基礎，掌握技巧的使用，然後再進行統計應用和技巧價值的解釋。每個章節結束時的方法練習強調計算和公式的使用，而應用練習要求學生將所學統計知識應用於現實問題。
●大幅擴展的R軟件支持使學生能夠使用這個重要的工具。修訂的電子書和WebAssign數字章節附錄現在包含相關的示例，以便輕鬆參考。此外，所有腳本都已更新，以確保與最新版本的R兼容。作者還擴展了腳本和.csv數據集的數量，以支持主要章節的示例、應用問題和案例。
●重新組織和擴展的迴歸分析和模型構建（第16章）充分解釋了概念。作者在整個章節中加強了內容。例如，作者增加了一個討論，比較了使用轉換後的因變量的迴歸模型與使用未轉換的原始單位的迴歸模型。此外，本章還包括一個新的示例，說明了使用Durbin-Watson統計量來檢驗一階自相關的存在。
●新的問題、案例和短篇故事使內容保持新鮮和時下。本版新增了100多個新問題和三個新案例。此外，作者還增加了三個新的實踐統計短篇故事，反映了統計學中的當前挑戰。
●更新的JMP章節附錄反映了JMP統計軟件的最新版本。無論是印刷版還是電子書中的所有JMP章節附錄都包含對最新的JMP®學生版16進行的更改。您可以確保學生能夠使用最新的統計數字支持。
●備受信賴的傑出作者團隊確保最準確、經過驗證的呈現方式。本版的作者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導者和活躍顧問，他們無縫合作，提供準確、現實世界的統計概念呈現，您可以信賴其準確性和全面、引人入勝的內容。
●WebAssign課程管理解決方案為商業統計提供了全面的教學工具。這個靈活且可完全自定義的平台為您提供了強大的節省時間的工具。您可以輕鬆部署作業，即時評估個別學生和班級表現，並幫助有困難的學生掌握課程概念。通過WebAssign強大的數字平台和本版的特定內容，您可以根據各種作業設置來定制課程。添加自己的問題和內容，並訪問學生和課程分析以及溝通工具。
●新的學習目標引起學生對關鍵概念的注意。本版的學習目標引導學生關注關鍵概念。
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是威克森林大學商學院的Inmar總統主席和商業分析課程的高級副院長。他出生於俄亥俄州辛辛那提市，擁有賽維爾大學（俄亥俄州）的學士學位和克萊姆森大學的博士學位。在加入威克森林大學教職之前，Camm博士曾在辛辛那提大學任教。他還曾在斯坦福大學擔任訪問學者，並在達特茅斯學院塔克商學院擔任商業管理訪問教授。Camm博士在優化應用於運營管理和市場營銷問題領域發表了45多篇論文。他的研究發表在《科學》、《管理科學》、《運營研究》、《界面》和其他專業期刊上。Camm博士曾獲得辛辛那提大學的Dornoff卓越教學研究員稱號，並於2006年獲得INFORMS運營研究實踐教學獎。作為實踐所言的堅定信徒，他曾擔任多家公司和政府機構的運營研究顧問。從2005年到2010年，Camm博士擔任INFORMS應用分析學報（前身為Interfaces）的主編。2017年，他被任命為INFORMS院士。
James J. Cochran是阿拉巴馬大學應用統計學教授，Rogers-Spivey教職研究員和教職員研究副院長。他出生於俄亥俄州代頓市，獲得萊特州立大學的學士、碩士和工商管理碩士學位，以及辛辛那提大學的博士學位。Cochran博士自2014年起在阿拉巴馬大學任職，並曾在斯坦福大學、塔爾卡大學、南非大學和康卡迪亞大學進行訪問學者研究。Cochran博士在運營研究和統計方法的發展和應用方面發表了45多篇論文。他的研究發表在《管理科學》、《美國統計學家》、《統計學與機率通訊》、《運營研究年鑒》、《歐洲運營研究期刊》、《組合優化學報》、《INFORMS應用分析和統計學》以及《統計學和概率信函》等期刊上。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年當選國際統計學會會員，並於2011年成為美國統計學會院士。他於2014年獲得創始人獎和2015年獲得卡爾·E·皮斯獎，這兩個獎項都是由美國統計學會頒發的。2017年，他獲得美國統計學會的Waller杰出教學生涯獎，並被任命為INFORMS院士。2018年，他獲得INFORMS主席獎。Cochran博士是有效統計和運營研究教育的堅定支持者，認為這是改善實際問題應用質量的手段，他組織並主持了世界各地的教學研討會。
Michael J. Fry是辛辛那提大學林德納商學院的運營、商業分析和信息系統教授，商業分析中心的學術主任。他出生於德克薩斯州基林市，獲得德克薩斯農工大學的學士學位和密歇根大學的碩士和博士學位。他自2002年起在辛辛那提大學任職，曾擔任系主任。Fry博士被任命為林德納研究研究員。他還曾在康奈爾大學塞繆爾·柯蒂斯·約翰遜研究生管理學院和英屬哥倫比亞大學Sauder商學院擔任訪問教授。Fry博士在《運營研究》、《M&SOM》、《Tra》等期刊上發表了25多篇研究論文。
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. 數據與統計。
2. 描述性統計：表格和圖形展示。
3. 描述性統計：數值測量。
4. 概率入門。
5. 離散概率分佈。
6. 連續概率分佈。
7. 抽樣和抽樣分佈。
8. 區間估計。
9. 假設檢驗。
10. 關於兩個母體的平均值和比例的推論。
11. 關於母體變異數的推論。
12. 比較多個比例、獨立性檢驗和適合度檢驗。
13. 實驗設計和變異數分析。
14. 簡單線性回歸。
15. 多元回歸。
16. 回歸分析：模型建立。
17. 時間序列分析和預測。
18. 非參數方法。
19. 決策分析。
20. 指數數字。
21. 品質控制的統計方法。
22. 樣本調查。
附錄A. 參考文獻。
附錄B. 表格。
附錄C. 總和符號。
附錄D. Microsoft Excel和統計分析工具。
附錄E. 使用JMP和Excel計算p值。
附錄F. Microsoft Excel線上和統計分析工具。
附錄G：偶數編號練習的解答（Cengage電子書）。
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