Modern Business Statistics

Modern Business Statistics pdf epub mobi txt 电子书 下载 2026

出版者:
作者:Anderson, David R./ Sweeney, Dennis J./ Williams, Thomas A.
出品人:
页数:940
译者:
出版时间:2008-6
价格:$ 238.37
装帧:
isbn号码:9780324598278
丛书系列:
图书标签:
  • 统计学
  • 商业统计
  • 数据分析
  • 概率论
  • 回归分析
  • 假设检验
  • 统计推断
  • 管理学
  • 经济学
  • 数据科学
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具体描述

Gain a strong conceptual understanding of statistics with the third edition of MODERN BUSINESS STATISTICS?s balance of real-world applications and focus on the integrated strengths of Microsoft? Excel? 2007. To ensure your understanding, this best-selling, comprehensive text carefully discusses and clearly develops each statistical technique in a solid application setting. Immediately after each easy-to-follow presentation of a statistical procedure, a subsection discusses how to use Excel? to perform the procedure. This integrated approach emphasizes the applications of Excel? while maintaining a focus on the statistical methodology. Step-by-step instructions and screen captures further clarify the presentation to ensure your understanding. A wealth of timely business examples, proven methods, and application exercises clearly demonstrate how statistical results provide insights into business decisions and present solutions to contemporary business problems. The book?s class-tested problem-scenario approach emphasizes how you can apply statistical methods to today?s practical business situations. New case problems and self-tests throughout this edition allow you to check your personal understanding. Additional learning resources, including CengageNOW? for online homework assistance and a complete support Website, provide everything you need for the Excel? 2007 skills and understanding of business statistics that is simply EXCEL?lent!

战略商业分析与数据驱动决策:面向未来商业领袖的实践指南 本书聚焦于将复杂的数据转化为可执行的商业洞察,是为致力于在竞争激烈的市场中取得领先地位的商业专业人士、经理人以及决策者量身打造的深度实用手册。 现代商业环境正经历着由信息爆炸驱动的深刻变革,企业生存的关键不再仅仅依赖直觉或经验,而是取决于其驾驭数据、预测趋势和优化流程的效率。本书旨在填补传统商业理论与前沿数据分析技术之间的鸿沟,提供一套系统化、可操作的分析框架,助力读者从“数据迷雾”中提炼出清晰的战略方向。 第一部分:商业分析的战略基石 本部分奠定坚实的战略思维基础,强调分析工作必须紧密围绕核心商业目标展开。我们首先探讨商业智能(BI)与商业分析(BA)的生态系统,区分两者的目标、工具集与应用场景。我们将深入分析如何构建数据驱动的组织文化,克服数据孤岛和抵触情绪,确保分析成果能有效融入日常运营。 重点内容包括: 战略对齐与问题界定(Problem Framing): 如何将模糊的商业挑战(如“如何提高市场份额”)转化为清晰、可量化的分析问题(如“在现有客户群中,哪些特征的客户最有可能在未来六个月内流失,其流失概率是多少?”)。我们介绍结构化的问题分解技术,确保分析投入产生最大的战略回报。 关键绩效指标(KPI)的科学设计: 超越表面的指标堆砌,本书教授如何设计一套真正反映业务健康状况和战略进展的指标体系。涵盖领先指标(Leading Indicators)与滞后指标(Lagging Indicators)的平衡,以及如何运用平衡计分卡(BSC)的原理将财务、客户、内部流程和学习与成长维度整合起来。 数据治理与伦理考量: 深入探讨数据质量对分析结果的决定性影响。内容涵盖数据生命周期管理、元数据管理,以及在数据收集、存储和使用过程中必须遵守的隐私保护法规(如GDPR/CCPA的商业影响)和商业伦理准则,确保分析的可靠性和合规性。 第二部分:核心分析技术与模型应用 本部分是本书的技术核心,它侧重于介绍和演示在商业决策中最为有效和普遍应用的分析方法论,强调“何时使用何种模型”以及“如何解读模型结果以支持决策”。 描述性分析的深度挖掘: 不仅仅是制作图表,而是深入理解业务的“是什么”和“为什么”。重点介绍时间序列分解(识别趋势、季节性和周期性)、分布分析(理解客户行为的变异性)和细分市场分析(使用聚类方法对客户进行有效分组,而非基于经验的主观划分)。 预测性分析:回归模型在商业中的实战应用: 详细讲解线性回归、多元回归在销售预测、定价优化和资源分配中的具体应用。特别强调模型诊断的重要性——如何识别多重共线性、异方差性,并进行稳健的模型选择。同时,介绍非线性回归在处理复杂回报率问题时的优势。 概率论与决策树:量化不确定性: 商业决策充满不确定性。本章引入贝叶斯思维,用于在获得新信息后更新决策信念。重点讲解决策树(Decision Trees)在评估投资组合风险、新产品发布决策中的作用,以及如何通过蒙特卡洛模拟来量化复杂项目在各种市场条件下的潜在收益和风险范围。 优化与资源配置: 介绍线性规划(Linear Programming)基础,用于解决供应链管理中的成本最小化问题(如运输路径优化)或生产计划中的利润最大化问题。提供实际案例,演示如何将资源约束转化为数学模型,并利用求解器得出最优解。 第三部分:面向特定商业职能的深度分析 本部分将分析工具箱与具体的商业功能场景相结合,展示分析如何直接转化为竞争优势。 客户关系管理(CRM)分析: 客户终身价值(CLV)的精确估算: 介绍不同生命周期模型(如Pareto/NBD模型)如何帮助企业识别高价值客户群体,并指导营销预算的分配。 客户流失预测(Churn Prediction): 深入探讨分类模型(如逻辑回归、支持向量机)在识别高风险客户方面的应用,并结合生存分析(Survival Analysis)来理解客户流失的时间维度。 推荐系统基础: 解释协同过滤和基于内容的推荐机制,以及它们如何驱动交叉销售和向上销售。 运营与供应链分析: 库存管理优化: 应用EOQ模型(经济订货量)和再订货点(ROP)策略,平衡持有成本与缺货风险。介绍如何利用需求波动数据建立更灵活的安全库存策略。 流程效率分析: 引入假设检验来评估流程改进措施的有效性(如A/B测试在网站改版或新流程试点中的应用),确保改进是基于统计显著性的,而非偶然波动。 金融与风险分析: 财务比率的动态分析: 如何使用统计方法分析关键财务比率随时间的变化趋势,识别潜在的财务压力点。 信用风险初探: 介绍构建简单评分卡(Scorecard)的基本逻辑,用于评估贷款申请人的违约概率。 第四部分:从洞察到行动:沟通与实施 再完美的分析,如果不能被有效沟通和执行,价值也等于零。本部分是连接“技术”与“商业结果”的桥梁。 讲故事的能力(Data Storytelling): 教授如何将复杂的统计输出转化为清晰、有说服力的叙事。重点在于识别听众的需求,选择最合适的视觉化工具(超越基础柱状图和饼图),并构建清晰的“情境-冲突-解决方案”结构来驱动管理层的行动。 构建交互式仪表板(Dashboards): 探讨设计高效、用户友好的仪表板的原则。内容包括信息密度控制、用户体验(UX)设计在数据可视化中的重要性,以及如何确保关键指标的“一目了然性”。 分析驱动的变革管理: 讨论如何成功地将分析建议嵌入到现有的业务流程中。这包括建立反馈循环机制,定期审计分析模型的准确性,并为业务团队提供持续的分析支持,确保数据驱动的决策成为企业的“新常态”。 本书的独特价值在于其对商业实践的深度承诺。 它不满足于展示公式,而是聚焦于商业语境下的模型选择、假设验证以及结果的战略解读。通过大量贴近真实商业场景的案例研究和实践练习,读者将能够建立起从原始数据到战略落地的完整分析能力链条,从而自信地领导企业穿越不确定的未来。

作者简介

David R. Anderson is Professor of Quantitative Analysis in the College of Business Administration at the University of Cincinnati. Born in Grand Forks, North Dakota, he earned his BS, MS, and PhD degrees from Purdue University. Professor Anderson has served as Head of the Department of Quantitative Analysis and Operations Management and as Associate Dean of the College of Business Administration. In addition, he was the coordinator of the College's first Executive Program. In addition to teaching introductory statistics for business students, Dr. Anderson has taught graduate-level courses in regression analysis, multivariate analysis, and management science. He also has taught statistical courses at the Department of Labor in Washington, D.C. Professor Anderson has been honored with nominations and awards for excellence in teaching and excellence in service to student organizations. He has coauthored ten textbooks related to decision sciences and actively consults with businesses in the areas of sampling and statistical methods.

Dennis J. Sweeney is Professor of Quantitative Analysis and founder of the Center for Productivity Improvement at the University of Cincinnati. Born in Des Moines, Iowa, he earned BS and BA degrees from Drake University, graduating summa cum laude. He received his MBA and DBA 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. Professor Sweeney served five years as Head of the Department of Quantitative Analysis and four years as Associate Dean of the College of Business Administration at the University of Cincinnati. He has published more than 30 articles 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 MANAGEMENT SCIENCE, OPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING, DECISION SCIENCES, and other journals. Professor Sweeney has coauthored ten textbooks in the areas of statistics, management science, linear programming, and production and operations management.

Thomas A. Williams is Professor of Management Science in the College of Business at Rochester Institute of Technology (RIT). Born in Elmira, New York, he earned his BS degree at Clarkson University. He completed his graduate work at Rensselaer Polytechnic Institute, where he received his MS and PhD degrees. Before joining the College of Business at RIT, Professor Williams served for seven years as a faculty member in the College of Business Administration at the University of Cincinnati, where he developed the first undergraduate program in Information Systems. At RIT he was the first chair of the Decision Sciences Department. Professor Williams is the coauthor of 11 textbooks in the areas of management science, statistics, production and operations management, and mathematics. He has been a consultant for numerous Fortune 500 companies in areas ranging from the use of elementary data analysis to the development of large-scale regression models.

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