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【原文書】 書名：Discrete-Time Signal Processing 3/e IE 作者：Alan V. Oppenheim ISBN：9780132067096 出版社：PEARSON 內容簡介 　　For senior/graduate-level courses in Discrete-Time Signal Processing.THE definitive, authoritative text on DSP — ideal for those with an introductory-level knowledge of signals and systems. Written by prominent DSP pioneers, it provides thorough treatment of the fundamental theorems and properties of discrete-time linear systems, filtering, sampling, and discrete-time Fourier Analysis. By focusing on the general and universal concepts in discrete-time signal processing, it remains vital and relevant to the new challenges arising in the field.Access to the password-protected companion Website and myeBook is included with each new copy of Discrete-Time Signal Processing, Third Edition.Ch 1:IntroductionCh 2:Discrete Time-Signals and SystemsCh 3:The z-TransformCh 4:Sampling of Continuous-Time SignalsCh 5:Transform Analysis of Linear Time-Invariant SystemsCh 6:Structures for Discrete-Time SystemsCh 7:Filter Design TechniquesCh 8:The Discrete Fourier TransformCh 9:Computation of the Discrete Fourier TransformCh 10:Fourier Analysis of Signals Using the Discrete Fourier TransformCh 11:Parametric Signal ModelingCh 12:Discrete Hilbert TransformsCh 13:Cepstrum Analysis and Homomorphic DeconvolutionAppendix A. Random SignalsAppendix B. Continuous-Time FiltersAppendix C. Answers to Selected Basic Problems 目錄 Ch 1:Introduction Ch 2:Discrete Time-Signals and Systems Ch 3:The z-Transform Ch 4:Sampling of Continuous-Time Signals Ch 5:Transform Analysis of Linear Time-Invariant Systems Ch 6:Structures for Discrete-Time Systems Ch 7:Filter Design Techniques Ch 8:The Discrete Fourier Transform Ch 9:Computation of the Discrete Fourier Transform Ch 10:Fourier Analysis of Signals Using the Discrete Fourier Transform Ch 11:Parametric Signal Modeling Ch 12:Discrete Hilbert Transforms Ch 13:Cepstrum Analysis and Homomorphic Deconvolution Appendix A. Random Signals Appendix B. Continuous-Time Filters Appendix C. Answers to Selected Basic Problems

Discrete Mathematics and Its Applications 8/e (Annotated Edition) 導讀本 (8版)

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書名：Discrete Mathematics and Its Applications 8/E(離散數學導讀本) 作者：Rosen 出版社：麥格羅希爾 出版日期：2020/01/00 ISBN：9789863414254 內容簡介 離散數學是數學、計算機科學及電機工程的必修科目，也是其他工程相關系所的必選科目之一。而Kenneth H. Rosen教授所編寫的離散數學一書，自1988年發行初版以來，如今已修訂至第八版，本書挑選的十三章內容中，除了有最基礎的理論性課題外，還包含了與實用相關的課題。 　本書非常強調數學推理以及各種解題方式，並以五大重要的主題貫穿全書： ♦數學推理：本書強調數學歸納法的技巧，藉甶各種不同形態的範例進行說明。 ♦組合分析：重點在於使用組合分析來解決計數問題，以及演算法的解析，而不是直接套用公式。 ♦離散結構：包括集合、排列、關係、圖形、樹圖及有限狀態機器。 ♦演算法思考：某些類別的問題可藉由演算法的明確敍述來解決。一旦被清楚說明之後，就可建構電腦程式予以執行。 ♦應用與模型化：本書探討許多關於資訊科學和資料網路方面的應用，另外還包括化學、植物學、動物學、語言學、地理、商業、網際網路等各種領域。 　除了課文之外，每章平均有超過百則的練習題，書末並附有奇數練習題之解答，能提供同學豐富且多元的練習機會。

總體經濟學 (1版)

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書名：Macroeconomics 10/E 作者：Abel/Bernanke/Croushore 出版社：PEARSON 出版日期：2020/03/00 ISBN：9781292318615 Table of Contents PART 1: INTRODUCTION 1. Introduction to Macroeconomics 2. The Measurement and Structure of the National Economy PART 2: LONG-RUN ECONOMIC PERFORMANCE 3. Productivity, Output, and Employment 4. Consumption, Saving, and Investment 5. Saving and Investment in the Open Economy 6. Long-Run Economic Growth 7. The Asset Market, Money, and Prices PART 3: BUSINESS CYCLES AND MACROECONOMIC POLICY 8. Business Cycles 9. The IS–LM/AD–AS Model: A General Framework for Macroeconomic Analysis 10. Classical Business Cycle Analysis: Market-Clearing Macroeconomics 11. Keynesianism: The Macroeconomics of Wage and Price Rigidity PART 4: MACROECONOMIC POLICY: ITS ENVIRONMENT AND INSTITUTIONS 12. Unemployment and Inflation 13. Exchange Rates, Business Cycles, and Macroeconomic Policy in the Open Economy 14. Monetary Policy and the Federal Reserve System 15. Government Spending and Its Financing Appendix A: Some Useful Analytical Tools

管理數學 (11版)

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【中文翻譯書】 書名：管理數學 第十一版 原文書名：Finite Mathematics for the Managerial, Life, and Social Sciences 11th Edition 原文作者：Soo T. Tan 中文譯者： 張純明 出版日期：2016/03/01 ISBN：9789865632410 目錄 第1章 直線和線性函數 第2章 線性方程組及矩陣 第3章 線性規劃：幾何方法 第4章 線性規劃：代數方法 第5章 財務數學 第6章 機率 第7章 馬可夫鏈與賽局理論 單數題解答

管理學 (14版)

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書名：管理學(14版) 作者：Robbins(洪緯典) 余坤東/審閱 出版社：華泰 出版日期：2019/09/00 ISBN：9789869781527 內容簡介 一、本書翻譯自Stephen P. Robbins與Mary Coulter所著《管理學》(Management 14e)。以組織、管理者、決策為核心，再以管理四大功能向外推展，內容結構清晰、條理分明、邏輯嚴謹，能為讀者建立完整的管理思維。 二、本書內容橫向擴展至全球觀點，縱向深入現代社會改變，包括：設計思考、大數據、職場多元性、社會責任、綠色管理、創造力、創新、創業家精神、分享經濟等，形成鼓勵讀者更進一步思考未來管理議題的現代管理學。 三、每章均提供摘要，協助讀者掌握關鍵概念；每章同時提供適合的個案，有助於讀者加深印象並掌握抽象概念、培養應用能力，並進行反思。 目錄 第一篇 管理學導論 第1章 工作環境中的管理者與你 第2章 決策制定 第二篇 現今工作環境下的管理 第3章 全球管理 第4章 重視多元的工作團隊 第5章 社會意識管理 第6章 變革管理 第7章 管理者的限制 第三篇 規劃 第8章 規劃與目標設定 第9章 策略規劃 第10章 培養創業家精神 第四篇 組織 第11章 組織設計 第12章 以團隊為核心的組織方式 第13章 人力資源管理 第五篇 領導 第14章 人際及組織溝通 第15章 組織行為 第16章 領導 第17章 激勵 第六篇 控制 第18章 控制組織活動及作業

Statistics for Business and Economics (15版)

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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. 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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. 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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）。

（福利品）STATISTICS FOR BUSINESS AND ECONOMICS (14版)

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【上緣撞傷有裁切過】 書名：Statistics for Business and Economics 14/e 作者：ANDERSON 出版社：CENGAGE 出版日期：2020/00/00 ISBN：9780357114483 內容簡介 ●TRUSTED TEAM OF DISTINGUISHED AUTHORS ENSURES THE MOST ACCURATE, PROVEN PRESENTATION. Prominent leaders and active consultants in business and statistics, authors David R. Anderson, Dennis J. Sweeney, Thomas A. Williams, Jeffrey D. Camm, James J. Cochran, Michael J. Fry and Jeffrey W. Ohlmann deliver an accurate, real-world presentation of statistical concepts you can trust with every edition. ●SYSTEMATIC APPROACH EMPHASIZES PROVEN METHODS AND APPLICATIONS. Students first develop a computational foundation and thoroughly 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 use of formulas, while Application Exercises require students to apply what they know about statistics to real-world problems 目錄 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 (MindTap Reader). Appendix A.References and Bibliography. Appendix B.Tables. Appendix C.Summation Notation. Appendix D.Answers to Even–Numbered Exercises (MindTap Reader). Appendix E.Microsoft Excel 2016 and Tools for Statistical Analysis. Appendix F.Computing p-Values Using JMP and Excel.

Statistics for Business and Economics (14版)

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Statistics for Business and Economics (GE)作  /  譯    者 ：James T. McClave,P. George Benson,Terry SincichI S B N -  13 ：9781292413396I S B N -  10 ：1292413395類             別：統計學 版             次：14 版年             份：2022規             格：896 頁出     版     商：Pearson Education 簡介 Now in its 14th edition, Statistics for Business and Economics by McClave, Benson, and Sincich places statistics in the context of contemporary business. The text places emphasis on inference, extensively covering data collection and analysis needed to evaluate the results of statistical studies and make good decisions. Students are encouraged to develop statistical thinking and to understand both the assessment of credibility and the value of data inferences. Based on the American Statistical Association's Guidelines for Assessment and Instruction in Statistics Education(GAISE) Project, the text emphasizes statistical literacy, fosters active learning in the classroom, discusses intuitive probability, stresses conceptual understanding over mere knowledge of procedures, and employs technology to develop this understanding and to analyze data. 目錄 Ch 1 Statistics, Data, and Statistical Thinking Ch 2 Methods for Describing Sets of Data Ch 3 Probability Ch 4 Random Variables and Probability Distributions Ch 5 Sampling Distributions Ch 6 Inferences Based on a Single Sample: Estimation with Confidence Intervals Ch 7 Inferences Based on a Single Sample: Tests of Hypotheses Ch 8 Inferences Based on Two Samples: Confidence Intervals and Tests of Hypotheses Ch 9 Design of Experiments and Analysis of Variance Ch10 Categorical Data Analysis Ch11 Simple Linear Regression Ch12 Multiple Regression and Model Building Ch13 Methods for Quality Improvement: Statistical Process Control (Available Online) Ch14 Time Series: Descriptive Analyses, Models, and Forecasting (Available Online) Ch15 Nonparametric Statistics (Available Online)

Essentials of Statistics for Business and Economics (10版)

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【簡介】 Learn how statistical information impacts decisions in today’s business world as Camm/Cochran/Fry/Ohlmann/Anderson/Sweeney/Williams' leading ESSENTIALS OF STATISTICS FOR BUSINESS AND ECONOMICS, 10E connects key concepts in each chapter to actual business practices. This edition combines clear statistical methods with a proven approach that presents a problem, then scenario. Updated applications reflect the latest developments in business and statistics. You work with more than 350 new and updated business examples, approximately 50 new and updated cases and hands-on exercises that highlight statistics in action. You also gain practice using leading professional statistical software with exercises and appendices that walk you through using Excel®, R and JMP® Student Edition. 【目錄】 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. 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.