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This course will provide a solid foundation in probability and statistics for students learning economics and management. We will emphasize topics needed for further study of econometrics and provide basic preparation for econometrics course. Topics include elements of probability theory, sampling theory, statistical inference, hypothesis testing, and linear regression.
本课程为经济管理类学生提供数理统计知识，为计量经济学的学习打下基础。课程主要介绍梳理统计的基本知识以及一些基本的统计推断方法。内容涵盖基本的概率论, 抽样, 统计推断, 假设检验, 线性回归。
The book connects concepts in each chapter to real-world practice. This edition delivers sound statistical methodology, a proven problem-scenario approach and meaningful applications that reflect the latest developments in business and statistics today. More than 350 new and proven real business examples, a wealth of practical cases and meaningful hands-on exercises highlight statistics in action.
The book emphasizes applications over calculation. It illustrates how vital statistical methods and tools are for today's managers--and teaches us how to apply them to real business problems. Using a proven three-step "ICI" approach to problem-solving, the text teaches us how to IDENTIFY the correct statistical technique by focusing on the problem objective and data type; how to COMPUTE the statistics doing them by hand, using Excel, or using MINITAB; and how to INTERPRET results in the context of the problem. This unique approach enhances our comprehension and practical skills. The text's vast assortment of data-driven examples, exercises, and cases covers the various functional areas of business, demonstrating the statistical applications that marketing managers, financial analysts, accountants, economists, and others use.
This applied introduction to probability and statistics reinforces basic mathematical concepts with numerous real-world examples and applications to illustrate the relevance of key concepts.
The revision of this well-respected text presents a balanced approach of the classical and Bayesian methods and now includes a chapter on simulation (including Markov chain Monte Carlo and the Bootstrap), coverage of residual analysis in linear models, and many examples using real data. Calculus is assumed as a prerequisite, and a familiarity with the concepts and elementary properties of vectors and matrices is a plus.
